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提交 1731b32d 编写于 作者: Z zlsh80826
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into trt_stack_op, test=develop

上级 b750d6ed 4adac0e3
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Showing with 2308 addition and 359 deletion
cmake/cudnn.cmake
浏览文件 @ 1731b32d
......@@ -60,9 +60,8 @@ else()
set(CUDNN_FOUND OFF)
endif()
if(CUDNN_FOUND)
file(READ ${CUDNN_INCLUDE_DIR}/cudnn.h CUDNN_VERSION_FILE_CONTENTS)
macro(find_cudnn_version cudnn_header_file)
file(READ ${cudnn_header_file} CUDNN_VERSION_FILE_CONTENTS)
get_filename_component(CUDNN_LIB_PATH ${CUDNN_LIBRARY} DIRECTORY)
string(REGEX MATCH "define CUDNN_VERSION +([0-9]+)"
......@@ -93,10 +92,15 @@ if(CUDNN_FOUND)
math(EXPR CUDNN_VERSION
"${CUDNN_MAJOR_VERSION} * 1000 +
${CUDNN_MINOR_VERSION} * 100 + ${CUDNN_PATCHLEVEL_VERSION}")
message(STATUS "Current cuDNN header is ${cudnn_header_file} "
"Current cuDNN version is v${CUDNN_MAJOR_VERSION}.${CUDNN_MINOR_VERSION}. ")
endif()
message(STATUS "Current cuDNN header is ${CUDNN_INCLUDE_DIR}/cudnn.h. "
"Current cuDNN version is v${CUDNN_MAJOR_VERSION}.${CUDNN_MINOR_VERSION}. ")
endif()
endmacro()
if(CUDNN_FOUND)
find_cudnn_version(${CUDNN_INCLUDE_DIR}/cudnn.h)
if (NOT CUDNN_MAJOR_VERSION)
find_cudnn_version(${CUDNN_INCLUDE_DIR}/cudnn_version.h)
endif()
endif()
paddle/fluid/imperative/partial_grad_engine.cc
浏览文件 @ 1731b32d
......@@ -885,12 +885,14 @@ void PartialGradTask::RunEachOp(OpBase *op) {
if (create_graph_) {
auto double_grad_node = CreateGradOpNode(op->InnerOp(), tmp_ins, tmp_outs,
op->Attrs(), op->place());
if (double_grad_node) {
VLOG(10) << "Create " << double_grad_node->size()
<< " double grad op(s) for " << op->Type()
<< ", pending ops: " << GradPendingOpTypes(*double_grad_node);
double_grad_nodes_.emplace_back(std::move(double_grad_node));
}
PADDLE_ENFORCE_NOT_NULL(
double_grad_node,
platform::errors::NotFound("The Op %s doesn't have any grad op.",
op->Type()));
VLOG(10) << "Create " << double_grad_node->size()
<< " double grad op(s) for " << op->Type()
<< ", pending ops: " << GradPendingOpTypes(*double_grad_node);
double_grad_nodes_.emplace_back(std::move(double_grad_node));
}
VLOG(10) << "There are " << grads_to_accumulate_.size() << " to sum gradient";
......
paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc
浏览文件 @ 1731b32d
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
......@@ -83,23 +80,10 @@ class EmbEltwiseLayerNormOpConverter : public OpConverter {
nvinfer1::ILayer* layer = nullptr;
if (engine_->with_dynamic_shape()) {
auto use_fp16 = engine_->WithFp16();
plugin::DynamicPluginTensorRT* plugin = nullptr;
if (use_fp16) {
#ifdef SUPPORTS_CUDA_FP16
plugin = new plugin::EmbEltwiseLayernormPluginDynamic<half>(
input_embs, bias, scale, emb_sizes, bias_size, scale_size, hidden,
eps);
#else
plugin = new plugin::EmbEltwiseLayernormPluginDynamic<float>(
input_embs, bias, scale, emb_sizes, bias_size, scale_size, hidden,
eps);
#endif
} else {
plugin = new plugin::EmbEltwiseLayernormPluginDynamic<float>(
input_embs, bias, scale, emb_sizes, bias_size, scale_size, hidden,
eps);
}
plugin = new plugin::EmbEltwiseLayernormPluginDynamic<float>(
input_embs, bias, scale, emb_sizes, bias_size, scale_size, hidden,
eps);
layer = engine_->AddPluginV2(input_ids.data(), input_num, plugin);
} else {
PADDLE_THROW(platform::errors::Fatal(
......
paddle/fluid/inference/tensorrt/engine.h
浏览文件 @ 1731b32d
......@@ -200,9 +200,23 @@ class TensorRTEngine {
void Deserialize(const std::string& engine_serialized_data) {
freshDeviceId();
infer_ptr<nvinfer1::IRuntime> runtime(createInferRuntime(&logger_));
infer_engine_.reset(runtime->deserializeCudaEngine(
engine_serialized_data.c_str(), engine_serialized_data.size(),
&inference::Singleton<plugin::PluginFactoryTensorRT>::Global()));
if (with_dynamic_shape_) {
#if IS_TRT_VERSION_GE(6000)
infer_engine_.reset(runtime->deserializeCudaEngine(
engine_serialized_data.c_str(), engine_serialized_data.size(),
nullptr));
#else
PADDLE_THROW(platform::errors::PreconditionNotMet(
"To enable dynamic shape support, the TensorRT version should be "
"greater than 6.0.0"));
#endif
} else {
infer_engine_.reset(runtime->deserializeCudaEngine(
engine_serialized_data.c_str(), engine_serialized_data.size(),
&inference::Singleton<plugin::PluginFactoryTensorRT>::Global()));
}
PADDLE_ENFORCE(infer_engine_ != nullptr,
"build cuda engine failed when deserialize engine info.!");
}
......
paddle/fluid/inference/tensorrt/helper.h
浏览文件 @ 1731b32d
......@@ -56,6 +56,9 @@ static nvinfer1::IRuntime* createInferRuntime(nvinfer1::ILogger* logger) {
return static_cast<nvinfer1::IRuntime*>(
dy::createInferRuntime_INTERNAL(logger, NV_TENSORRT_VERSION));
}
static nvinfer1::IPluginRegistry* getPluginRegistry() {
return static_cast<nvinfer1::IPluginRegistry*>(dy::getPluginRegistry());
}
// A logger for create TensorRT infer builder.
class NaiveLogger : public nvinfer1::ILogger {
......
paddle/fluid/inference/tensorrt/plugin/emb_eltwise_layernorm_plugin.cu
浏览文件 @ 1731b32d
......@@ -33,53 +33,29 @@ namespace plugin {
template <typename T>
int EmbEltwiseLayernormPluginDynamic<T>::initialize() {
int nb_emb = embs_.size();
std::vector<void *> ptr_vector(nb_emb);
std::vector<std::vector<half>> emb_fp16(nb_emb);
if (sizeof(T) == sizeof(float)) {
// FP32
for (int i = 0; i < nb_emb; ++i) {
ptr_vector[i] = embs_[i];
}
} else {
// FP16
for (int i = 0; i < nb_emb; ++i) {
auto emb_size = emb_sizes_[i];
auto &tmp = emb_fp16[i];
tmp.resize(emb_size);
for (int j = 0; j < emb_size; ++j) {
tmp[j] = static_cast<half>(embs_[i][j]);
}
ptr_vector[i] = tmp.data();
}
}
embs_gpu_.resize(embs_.size());
for (int i = 0; i < embs_.size(); i++) {
cudaMalloc(&embs_gpu_[i], sizeof(T) * emb_sizes_[i]);
cudaMemcpy(embs_gpu_[i], ptr_vector[i], emb_sizes_[i] * sizeof(T),
cudaMemcpyHostToDevice);
if (embs_[i]) {
cudaMalloc(&embs_gpu_[i], sizeof(float) * emb_sizes_[i]);
cudaMemcpy(embs_gpu_[i], embs_[i], emb_sizes_[i] * sizeof(float),
cudaMemcpyHostToDevice);
}
}
cudaMalloc(&bias_gpu_, sizeof(float) * bias_size_);
cudaMemcpy(bias_gpu_, bias_, bias_size_ * sizeof(float),
cudaMemcpyHostToDevice);
cudaMalloc(&scale_gpu_, sizeof(float) * scale_size_);
cudaMemcpy(scale_gpu_, scale_, scale_size_ * sizeof(float),
cudaMemcpyHostToDevice);
return 0;
}
if (bias_) {
cudaMalloc(&bias_gpu_, sizeof(float) * bias_size_);
cudaMemcpy(bias_gpu_, bias_, bias_size_ * sizeof(float),
cudaMemcpyHostToDevice);
}
if (scale_) {
cudaMalloc(&scale_gpu_, sizeof(float) * scale_size_);
cudaMemcpy(scale_gpu_, scale_, scale_size_ * sizeof(float),
cudaMemcpyHostToDevice);
}
template <typename T>
size_t EmbEltwiseLayernormPluginDynamic<T>::getSerializationSize() const {
return 0;
}
template <typename T>
void EmbEltwiseLayernormPluginDynamic<T>::serialize(void *buffer) const {}
template <typename T>
nvinfer1::DimsExprs EmbEltwiseLayernormPluginDynamic<T>::getOutputDimensions(
int output_index, const nvinfer1::DimsExprs *inputs, int nb_inputs,
......
paddle/fluid/inference/tensorrt/plugin/emb_eltwise_layernorm_plugin.h
浏览文件 @ 1731b32d
......@@ -44,8 +44,42 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
hidden_size_(hidden_size),
eps_(eps) {}
EmbEltwiseLayernormPluginDynamic(void const* serialData,
size_t serialLength) {}
EmbEltwiseLayernormPluginDynamic(void const* serial_data,
size_t serial_length) {
DeserializeValue(&serial_data, &serial_length, &emb_sizes_);
embs_gpu_.resize(emb_sizes_.size());
embs_.resize(emb_sizes_.size());
for (size_t i = 0; i < emb_sizes_.size(); i++) {
cudaMalloc(&embs_gpu_[i], sizeof(float) * emb_sizes_[i]);
cudaMemcpy(embs_gpu_[i], serial_data, emb_sizes_[i] * sizeof(float),
cudaMemcpyHostToDevice);
reinterpret_cast<char const*&>(serial_data) +=
emb_sizes_[i] * sizeof(float);
serial_length -= emb_sizes_[i] * sizeof(float);
embs_[i] = nullptr;
}
DeserializeValue(&serial_data, &serial_length, &bias_size_);
DeserializeValue(&serial_data, &serial_length, &scale_size_);
cudaMalloc(&bias_gpu_, sizeof(float) * bias_size_);
cudaMemcpy(bias_gpu_, serial_data, bias_size_ * sizeof(float),
cudaMemcpyHostToDevice);
bias_ = nullptr;
reinterpret_cast<char const*&>(serial_data) += bias_size_ * sizeof(float);
serial_length -= bias_size_ * sizeof(float);
cudaMalloc(&scale_gpu_, sizeof(float) * scale_size_);
cudaMemcpy(scale_gpu_, serial_data, scale_size_ * sizeof(float),
cudaMemcpyHostToDevice);
scale_ = nullptr;
reinterpret_cast<char const*&>(serial_data) += scale_size_ * sizeof(float);
serial_length -= scale_size_ * sizeof(float);
DeserializeValue(&serial_data, &serial_length, &hidden_size_);
DeserializeValue(&serial_data, &serial_length, &eps_);
}
nvinfer1::IPluginV2DynamicExt* clone() const override {
return new EmbEltwiseLayernormPluginDynamic(
embs_, bias_, scale_, emb_sizes_, bias_size_, scale_size_, hidden_size_,
......@@ -58,36 +92,66 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
int getNbOutputs() const override { return 1; }
int initialize() override;
size_t getSerializationSize() const override;
void serialize(void* buffer) const override;
size_t getSerializationSize() const override {
int sum_num = 0;
sum_num += SerializedSize(emb_sizes_);
for (size_t i = 0; i < emb_sizes_.size(); i++) {
sum_num += emb_sizes_[i] * sizeof(float);
}
sum_num += SerializedSize(bias_size_);
sum_num += SerializedSize(scale_size_);
sum_num += (bias_size_ + scale_size_) * sizeof(float);
sum_num += SerializedSize(hidden_size_);
sum_num += SerializedSize(eps_);
// sum_num += SerializedSize(with_fp16_);
return sum_num;
}
void serialize(void* buffer) const override {
// SerializeValue(&buffer, with_fp16_);
SerializeValue(&buffer, emb_sizes_);
for (size_t i = 0; i < emb_sizes_.size(); i++) {
SerializeCudaPointer(&buffer, embs_gpu_[i], emb_sizes_[i]);
}
SerializeValue(&buffer, bias_size_);
SerializeValue(&buffer, scale_size_);
SerializeCudaPointer(&buffer, bias_gpu_, bias_size_);
SerializeCudaPointer(&buffer, scale_gpu_, scale_size_);
SerializeValue(&buffer, hidden_size_);
SerializeValue(&buffer, eps_);
}
nvinfer1::DimsExprs getOutputDimensions(
int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs,
nvinfer1::IExprBuilder& expr_builder) override;
bool supportsFormatCombination(int pos,
const nvinfer1::PluginTensorDesc* inOut,
int nbInputs, int nbOutputs) override;
const nvinfer1::PluginTensorDesc* in_out,
int nb_inputs, int nb_outputs) override;
void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in,
int nbInputs,
int nb_inputs,
const nvinfer1::DynamicPluginTensorDesc* out,
int nbOutputs) override {}
int nb_outputs) override {}
size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs,
int nbInputs,
int nb_inputs,
const nvinfer1::PluginTensorDesc* outputs,
int nbOutputs) const override {
int nb_outputs) const override {
return 0;
}
int enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
const nvinfer1::PluginTensorDesc* outputDesc,
int enqueue(const nvinfer1::PluginTensorDesc* input_desc,
const nvinfer1::PluginTensorDesc* output_desc,
const void* const* inputs, void* const* outputs, void* workspace,
cudaStream_t stream) override;
nvinfer1::DataType getOutputDataType(int index,
const nvinfer1::DataType* inputTypes,
int nbInputs) const override;
const nvinfer1::DataType* input_types,
int nb_inputs) const override;
void destroy() override { delete this; }
......@@ -99,7 +163,7 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
// data on devices
float* bias_gpu_;
float* scale_gpu_;
std::vector<T*> embs_gpu_;
std::vector<float*> embs_gpu_;
std::vector<int> emb_sizes_;
int bias_size_;
......@@ -107,6 +171,49 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
int hidden_size_;
float eps_;
};
class EmbEltwiseLayernormPluginV2Creator : public nvinfer1::IPluginCreator {
public:
EmbEltwiseLayernormPluginV2Creator() {}
const char* getPluginName() const override {
return "fused_embedding_eltwise_layernorm_plugin";
}
const char* getPluginVersion() const override { return "1"; }
const nvinfer1::PluginFieldCollection* getFieldNames() override {
return &field_collection_;
}
nvinfer1::IPluginV2* createPlugin(
const char* name, const nvinfer1::PluginFieldCollection* fc) override {
return nullptr;
}
nvinfer1::IPluginV2* deserializePlugin(const char* name,
const void* serial_data,
size_t serial_length) override {
return new EmbEltwiseLayernormPluginDynamic<float>(serial_data,
serial_length);
}
void setPluginNamespace(const char* lib_namespace) override {
plugin_namespace_ = lib_namespace;
}
const char* getPluginNamespace() const override {
return plugin_namespace_.c_str();
}
private:
std::string plugin_namespace_;
std::string plugin_name_;
nvinfer1::PluginFieldCollection field_collection_;
std::vector<nvinfer1::PluginField> plugin_attributes_;
};
REGISTER_TRT_PLUGIN_V2(EmbEltwiseLayernormPluginV2Creator);
#endif
} // namespace plugin
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/plugin/gelu_op_plugin.cu
浏览文件 @ 1731b32d
......@@ -132,9 +132,6 @@ int GeluPlugin::enqueue(int batch_size, const void* const* inputs,
// Dynamic Plugin below.
#if IS_TRT_VERSION_GE(6000)
size_t GeluPluginDynamic::getSerializationSize() const { return 0; }
void GeluPluginDynamic::serialize(void* buffer) const {}
nvinfer1::DimsExprs GeluPluginDynamic::getOutputDimensions(
int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs,
......
paddle/fluid/inference/tensorrt/plugin/gelu_op_plugin.h
浏览文件 @ 1731b32d
......@@ -30,8 +30,8 @@ class GeluPlugin : public PluginTensorRT {
// It was used for tensorrt deserialization.
// It should not be called by users.
GeluPlugin(void const* serialData, size_t serialLength) {
deserializeBase(serialData, serialLength);
GeluPlugin(void const* serial_data, size_t serial_length) {
deserializeBase(serial_data, serial_length);
}
~GeluPlugin() {}
......@@ -43,8 +43,8 @@ class GeluPlugin : public PluginTensorRT {
bool supportsFormat(nvinfer1::DataType type,
nvinfer1::PluginFormat format) const override;
nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs,
int nbInputDims) override;
int enqueue(int batchSize, const void* const* inputs, void** outputs,
int nb_input_dims) override;
int enqueue(int batch_size, const void* const* inputs, void** outputs,
void* workspace, cudaStream_t stream) override;
protected:
......@@ -64,7 +64,7 @@ class GeluPlugin : public PluginTensorRT {
class GeluPluginDynamic : public DynamicPluginTensorRT {
public:
GeluPluginDynamic() {}
GeluPluginDynamic(void const* serialData, size_t serialLength) {}
GeluPluginDynamic(void const* serial_data, size_t serial_length) {}
~GeluPluginDynamic() {}
nvinfer1::IPluginV2DynamicExt* clone() const override {
......@@ -75,39 +75,79 @@ class GeluPluginDynamic : public DynamicPluginTensorRT {
int getNbOutputs() const override { return 1; }
int initialize() override { return 0; }
size_t getSerializationSize() const override;
void serialize(void* buffer) const override;
size_t getSerializationSize() const override { return 0; }
void serialize(void* buffer) const override {}
nvinfer1::DimsExprs getOutputDimensions(
int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs,
nvinfer1::IExprBuilder& exprBuilder) override;
int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs,
nvinfer1::IExprBuilder& expr_builder) override;
bool supportsFormatCombination(int pos,
const nvinfer1::PluginTensorDesc* inOut,
int nbInputs, int nbOutputs) override;
const nvinfer1::PluginTensorDesc* in_out,
int nb_inputs, int nb_outputs) override;
void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in,
int nbInputs,
int nb_inputs,
const nvinfer1::DynamicPluginTensorDesc* out,
int nbOutputs) override {}
int nb_outputs) override {}
size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs,
int nbInputs,
int nb_inputs,
const nvinfer1::PluginTensorDesc* outputs,
int nbOutputs) const override {
int nb_outputs) const override {
return 0;
}
int enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
const nvinfer1::PluginTensorDesc* outputDesc,
int enqueue(const nvinfer1::PluginTensorDesc* input_desc,
const nvinfer1::PluginTensorDesc* output_desc,
const void* const* inputs, void* const* outputs, void* workspace,
cudaStream_t stream) override;
nvinfer1::DataType getOutputDataType(int index,
const nvinfer1::DataType* inputTypes,
int nbInputs) const override;
const nvinfer1::DataType* input_types,
int nb_inputs) const override;
void destroy() override { delete this; }
};
class GeluPluginV2Creator : public nvinfer1::IPluginCreator {
public:
GeluPluginV2Creator() {}
const char* getPluginName() const override { return "gelu_plugin"; }
const char* getPluginVersion() const override { return "1"; }
const nvinfer1::PluginFieldCollection* getFieldNames() override {
return &field_collection_;
}
nvinfer1::IPluginV2* createPlugin(
const char* name, const nvinfer1::PluginFieldCollection* fc) override {
return nullptr;
}
nvinfer1::IPluginV2* deserializePlugin(const char* name,
const void* serial_data,
size_t serial_length) override {
auto plugin = new GeluPluginDynamic(serial_data, serial_length);
return plugin;
}
void setPluginNamespace(const char* lib_namespace) override {
plugin_namespace_ = lib_namespace;
}
const char* getPluginNamespace() const override {
return plugin_namespace_.c_str();
}
private:
std::string plugin_namespace_;
std::string plugin_name_;
nvinfer1::PluginFieldCollection field_collection_{0, nullptr};
std::vector<nvinfer1::PluginField> plugin_attributes_;
};
REGISTER_TRT_PLUGIN_V2(GeluPluginV2Creator);
#endif
} // namespace plugin
......
paddle/fluid/inference/tensorrt/plugin/qkv_to_context_plugin.cu
浏览文件 @ 1731b32d
......@@ -152,10 +152,6 @@ inline void TransposeQKV(const int batch, const int seq_len,
int QkvToContextPluginDynamic::initialize() { return 0; }
size_t QkvToContextPluginDynamic::getSerializationSize() const { return 0; }
void QkvToContextPluginDynamic::serialize(void *buffer) const {}
nvinfer1::DimsExprs QkvToContextPluginDynamic::getOutputDimensions(
int output_index, const nvinfer1::DimsExprs *inputs, int nb_inputs,
nvinfer1::IExprBuilder &expr_builder) {
......
paddle/fluid/inference/tensorrt/plugin/qkv_to_context_plugin.h
浏览文件 @ 1731b32d
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
......@@ -37,7 +51,13 @@ class QkvToContextPluginDynamic : public DynamicPluginTensorRT {
scale_(scale),
ban_fp16_(ban_fp16) {}
QkvToContextPluginDynamic(void const* serialData, size_t serialLength) {}
QkvToContextPluginDynamic(void const* serial_data, size_t serial_length) {
DeserializeValue(&serial_data, &serial_length, &hidden_);
DeserializeValue(&serial_data, &serial_length, &head_number_);
DeserializeValue(&serial_data, &serial_length, &head_size_);
DeserializeValue(&serial_data, &serial_length, &scale_);
DeserializeValue(&serial_data, &serial_length, &ban_fp16_);
}
nvinfer1::IPluginV2DynamicExt* clone() const override {
return new QkvToContextPluginDynamic(hidden_, head_number_, head_size_,
scale_, ban_fp16_);
......@@ -47,26 +67,36 @@ class QkvToContextPluginDynamic : public DynamicPluginTensorRT {
int getNbOutputs() const override { return 1; }
int initialize() override;
size_t getSerializationSize() const override;
void serialize(void* buffer) const override;
size_t getSerializationSize() const override {
return SerializedSize(hidden_) + SerializedSize(head_number_) +
SerializedSize(head_size_) + SerializedSize(scale_) +
SerializedSize(ban_fp16_);
}
void serialize(void* buffer) const override {
SerializeValue(&buffer, hidden_);
SerializeValue(&buffer, head_number_);
SerializeValue(&buffer, head_size_);
SerializeValue(&buffer, scale_);
SerializeValue(&buffer, ban_fp16_);
}
nvinfer1::DimsExprs getOutputDimensions(
int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs,
nvinfer1::IExprBuilder& expr_builder) override;
bool supportsFormatCombination(int pos,
const nvinfer1::PluginTensorDesc* inOut,
int nbInputs, int nbOutputs) override;
const nvinfer1::PluginTensorDesc* in_out,
int nb_inputs, int nb_outputs) override;
void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in,
int nbInputs,
int nb_inputs,
const nvinfer1::DynamicPluginTensorDesc* out,
int nbOutputs) override {}
int nb_outputs) override {}
size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs,
int nbInputs,
int nb_inputs,
const nvinfer1::PluginTensorDesc* outputs,
int nbOutputs) const override {
int nb_outputs) const override {
return 0;
}
......@@ -75,8 +105,8 @@ class QkvToContextPluginDynamic : public DynamicPluginTensorRT {
const void* const* inputs, void* const* outputs, void* workspace,
cudaStream_t stream) override;
nvinfer1::DataType getOutputDataType(int index,
const nvinfer1::DataType* inputTypes,
int nbInputs) const override;
const nvinfer1::DataType* input_types,
int nb_inputs) const override;
void destroy() override { delete this; }
......@@ -87,6 +117,45 @@ class QkvToContextPluginDynamic : public DynamicPluginTensorRT {
float scale_;
bool ban_fp16_;
};
class QkvToContextPluginV2Creator : public nvinfer1::IPluginCreator {
public:
QkvToContextPluginV2Creator() {}
const char* getPluginName() const override { return "qkv_to_context_plugin"; }
const char* getPluginVersion() const override { return "1"; }
const nvinfer1::PluginFieldCollection* getFieldNames() override {
return &field_collection_;
}
nvinfer1::IPluginV2* createPlugin(
const char* name, const nvinfer1::PluginFieldCollection* fc) override {
return nullptr;
}
nvinfer1::IPluginV2* deserializePlugin(const char* name,
const void* serial_data,
size_t serial_length) override {
auto plugin = new QkvToContextPluginDynamic(serial_data, serial_length);
return plugin;
}
void setPluginNamespace(const char* lib_namespace) override {
plugin_namespace_ = lib_namespace;
}
const char* getPluginNamespace() const override {
return plugin_namespace_.c_str();
}
private:
std::string plugin_namespace_;
std::string plugin_name_;
nvinfer1::PluginFieldCollection field_collection_;
std::vector<nvinfer1::PluginField> plugin_attributes_;
};
REGISTER_TRT_PLUGIN_V2(QkvToContextPluginV2Creator);
#endif
} // namespace plugin
......
paddle/fluid/inference/tensorrt/plugin/skip_layernorm_op_plugin.cu
浏览文件 @ 1731b32d
......@@ -32,18 +32,14 @@ namespace plugin {
int SkipLayerNormPluginDynamic::initialize() {
cudaMalloc(&bias_gpu_, sizeof(float) * bias_size_);
cudaMemcpy(bias_gpu_, bias_, bias_size_ * sizeof(float),
cudaMemcpy(bias_gpu_, bias_.data(), bias_size_ * sizeof(float),
cudaMemcpyHostToDevice);
cudaMalloc(&scale_gpu_, sizeof(float) * scale_size_);
cudaMemcpy(scale_gpu_, scale_, scale_size_ * sizeof(float),
cudaMemcpy(scale_gpu_, scale_.data(), scale_size_ * sizeof(float),
cudaMemcpyHostToDevice);
return 0;
}
size_t SkipLayerNormPluginDynamic::getSerializationSize() const { return 0; }
void SkipLayerNormPluginDynamic::serialize(void *buffer) const {}
nvinfer1::DimsExprs SkipLayerNormPluginDynamic::getOutputDimensions(
int output_index, const nvinfer1::DimsExprs *inputs, int nb_inputs,
nvinfer1::IExprBuilder &expr_builder) {
......
paddle/fluid/inference/tensorrt/plugin/skip_layernorm_op_plugin.h
浏览文件 @ 1731b32d
......@@ -29,61 +29,84 @@ namespace plugin {
#if IS_TRT_VERSION_GE(6000)
class SkipLayerNormPluginDynamic : public DynamicPluginTensorRT {
public:
explicit SkipLayerNormPluginDynamic(float* bias, float* scale, int bias_size,
int scale_size, const float eps,
bool ban_fp16)
: bias_(bias),
scale_(scale),
bias_size_(bias_size),
explicit SkipLayerNormPluginDynamic(const float* bias, const float* scale,
int bias_size, int scale_size,
const float eps, bool ban_fp16)
: bias_size_(bias_size),
scale_size_(scale_size),
eps_(eps),
ban_fp16_(ban_fp16) {}
SkipLayerNormPluginDynamic(void const* serialData, size_t serialLength) {}
ban_fp16_(ban_fp16) {
bias_.resize(bias_size);
scale_.resize(scale_size);
std::copy(bias, bias + bias_size, bias_.data());
std::copy(scale, scale + scale_size, scale_.data());
}
SkipLayerNormPluginDynamic(void const* serial_data, size_t serial_length) {
DeserializeValue(&serial_data, &serial_length, &bias_);
DeserializeValue(&serial_data, &serial_length, &scale_);
DeserializeValue(&serial_data, &serial_length, &bias_size_);
DeserializeValue(&serial_data, &serial_length, &scale_size_);
DeserializeValue(&serial_data, &serial_length, &eps_);
DeserializeValue(&serial_data, &serial_length, &ban_fp16_);
}
nvinfer1::IPluginV2DynamicExt* clone() const override {
return new SkipLayerNormPluginDynamic(bias_, scale_, bias_size_,
scale_size_, eps_, ban_fp16_);
return new SkipLayerNormPluginDynamic(
bias_.data(), scale_.data(), bias_size_, scale_size_, eps_, ban_fp16_);
}
const char* getPluginType() const override { return "skip_layernorm_plugin"; }
int getNbOutputs() const override { return 1; }
int initialize() override;
size_t getSerializationSize() const override;
void serialize(void* buffer) const override;
size_t getSerializationSize() const override {
size_t ser_size = SerializedSize(bias_) + SerializedSize(scale_) +
SerializedSize(bias_size_) + SerializedSize(scale_size_) +
SerializedSize(eps_) + SerializedSize(eps_);
return ser_size;
}
void serialize(void* buffer) const override {
SerializeValue(&buffer, bias_);
SerializeValue(&buffer, scale_);
SerializeValue(&buffer, bias_size_);
SerializeValue(&buffer, scale_size_);
SerializeValue(&buffer, eps_);
SerializeValue(&buffer, ban_fp16_);
}
nvinfer1::DimsExprs getOutputDimensions(
int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs,
nvinfer1::IExprBuilder& expr_builder) override;
bool supportsFormatCombination(int pos,
const nvinfer1::PluginTensorDesc* inOut,
int nbInputs, int nbOutputs) override;
const nvinfer1::PluginTensorDesc* in_out,
int nb_inputs, int nb_outputs) override;
void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in,
int nbInputs,
int nb_inputs,
const nvinfer1::DynamicPluginTensorDesc* out,
int nbOutputs) override {}
int nb_outputs) override {}
size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs,
int nbInputs,
int nb_inputs,
const nvinfer1::PluginTensorDesc* outputs,
int nbOutputs) const override {
int nb_outputs) const override {
return 0;
}
int enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
const nvinfer1::PluginTensorDesc* outputDesc,
int enqueue(const nvinfer1::PluginTensorDesc* input_desc,
const nvinfer1::PluginTensorDesc* output_desc,
const void* const* inputs, void* const* outputs, void* workspace,
cudaStream_t stream) override;
nvinfer1::DataType getOutputDataType(int index,
const nvinfer1::DataType* inputTypes,
int nbInputs) const override;
const nvinfer1::DataType* input_types,
int nb_inputs) const override;
void destroy() override { delete this; }
private:
float* bias_;
float* scale_;
std::vector<float> bias_;
std::vector<float> scale_;
float* bias_gpu_;
float* scale_gpu_;
......@@ -94,6 +117,45 @@ class SkipLayerNormPluginDynamic : public DynamicPluginTensorRT {
float eps_;
bool ban_fp16_;
};
class SkipLayerNormPluginV2Creator : public nvinfer1::IPluginCreator {
public:
SkipLayerNormPluginV2Creator() {}
const char* getPluginName() const override { return "skip_layernorm_plugin"; }
const char* getPluginVersion() const override { return "1"; }
const nvinfer1::PluginFieldCollection* getFieldNames() override {
return &field_collection_;
}
nvinfer1::IPluginV2* createPlugin(
const char* name, const nvinfer1::PluginFieldCollection* fc) override {
return nullptr;
}
nvinfer1::IPluginV2* deserializePlugin(const char* name,
const void* serial_data,
size_t serial_length) override {
auto plugin = new SkipLayerNormPluginDynamic(serial_data, serial_length);
return plugin;
}
void setPluginNamespace(const char* lib_namespace) override {
plugin_namespace_ = lib_namespace;
}
const char* getPluginNamespace() const override {
return plugin_namespace_.c_str();
}
private:
std::string plugin_namespace_;
std::string plugin_name_;
nvinfer1::PluginFieldCollection field_collection_;
std::vector<nvinfer1::PluginField> plugin_attributes_;
};
REGISTER_TRT_PLUGIN_V2(SkipLayerNormPluginV2Creator);
#endif
} // namespace plugin
......
paddle/fluid/inference/tensorrt/plugin/trt_plugin.h
浏览文件 @ 1731b32d
......@@ -175,11 +175,24 @@ class DynamicPluginTensorRT : public nvinfer1::IPluginV2DynamicExt {
void serializeBase(void*& buffer) const; // NOLINT
private:
std::string name_space_{"paddle_trt"};
std::string plugin_base_{"plugin_dynamic"};
std::string name_space_;
std::string plugin_base_;
};
#endif
template <typename T>
class TrtPluginRegistrarV2 {
public:
TrtPluginRegistrarV2() { getPluginRegistry()->registerCreator(creator, ""); }
private:
T creator;
};
#define REGISTER_TRT_PLUGIN_V2(name) \
static paddle::inference::tensorrt::plugin::TrtPluginRegistrarV2<name> \
plugin_registrar_##name {}
} // namespace plugin
} // namespace tensorrt
} // namespace inference
......
paddle/fluid/inference/tensorrt/plugin/trt_plugin_utils.h
浏览文件 @ 1731b32d
......@@ -128,6 +128,12 @@ inline void DeserializeValue(void const** buffer, size_t* buffer_size,
return details::Serializer<T>::Deserialize(buffer, buffer_size, value);
}
template <typename T>
inline void SerializeCudaPointer(void** buffer, T* value, int size) {
cudaMemcpy((*buffer), value, size * sizeof(T), cudaMemcpyDeviceToHost);
reinterpret_cast<char*&>(*buffer) += size * sizeof(T);
}
} // namespace plugin
} // namespace tensorrt
} // namespace inference
......
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ 1731b32d
......@@ -433,6 +433,25 @@ if(WITH_GPU AND TENSORRT_FOUND)
inference_analysis_test(test_trt_dynamic_shape_ernie SRCS trt_dynamic_shape_ernie_test.cc
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
ARGS --infer_model=${TEST_TRT_ERNIE_MODEL}/ernie_model_4)
set(TEST_TRT_ERNIE_UNSER_MODEL "${TRT_MODEL_INSTALL_DIR}/ernie_test/ernie_model_4_unserialized/")
if (NOT EXISTS ${TEST_TRT_ERNIE_UNSER_MODEL})
inference_download_and_uncompress(${TEST_TRT_ERNIE_MODEL} ${INFERENCE_URL}/tensorrt_test "ernie_model_4_unserialized.tgz")
endif()
inference_analysis_test(test_trt_dynamic_shape_ernie_serialize SRCS trt_dynamic_shape_ernie_deserialize_test.cc
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
ARGS --infer_model=${TEST_TRT_ERNIE_MODEL}/ernie_model_4_unserialized)
set(TEST_TRT_ERNIE_SER_MODEL "${TRT_MODEL_INSTALL_DIR}/ernie_test/ernie_model_4_serialized/")
if (NOT EXISTS ${TEST_TRT_ERNIE_SER_MODEL})
inference_download_and_uncompress(${TEST_TRT_ERNIE_MODEL} ${INFERENCE_URL}/tensorrt_test "ernie_model_4_serialized.tgz")
endif()
inference_analysis_test(test_trt_dynamic_shape_ernie_deserialize SRCS trt_dynamic_shape_ernie_deserialize_test.cc
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
ARGS --infer_model=${TEST_TRT_ERNIE_MODEL}/ernie_model_4_serialized)
endif()
set(LITE_MODEL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/lite")
......
paddle/fluid/inference/tests/api/trt_dynamic_shape_ernie_deserialize_test.cc 0 → 100644
浏览文件 @ 1731b32d
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/fluid/inference/tests/api/trt_test_helper.h"
namespace paddle {
namespace inference {
void run(const AnalysisConfig& config, std::vector<float>* out_data) {
auto predictor = CreatePaddlePredictor(config);
auto input_names = predictor->GetInputNames();
int run_batch = 1;
const int run_seq_len = 128;
std::vector<int64_t> tmp_input;
std::vector<float> tmp_four_input;
tmp_input.reserve(run_batch * run_seq_len);
tmp_four_input.reserve(run_batch * run_seq_len);
int64_t i0[run_seq_len] = {
1, 3558, 4, 75, 491, 89, 340, 313, 93, 4, 255, 10, 75, 321,
4095, 1902, 4, 134, 49, 75, 311, 14, 44, 178, 543, 15, 12043, 2,
75, 201, 340, 9, 14, 44, 486, 218, 1140, 279, 12043, 2};
int64_t i1[run_seq_len] = {
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
int64_t i2[run_seq_len] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39};
float i3[run_seq_len] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
// first input
auto input_t = predictor->GetInputTensor(input_names[0]);
input_t->Reshape({run_batch, run_seq_len, 1});
input_t->copy_from_cpu(i0);
// second input
auto input_t2 = predictor->GetInputTensor(input_names[1]);
input_t2->Reshape({run_batch, run_seq_len, 1});
input_t2->copy_from_cpu(i1);
// third input.
auto input_t3 = predictor->GetInputTensor(input_names[2]);
input_t3->Reshape({run_batch, run_seq_len, 1});
input_t3->copy_from_cpu(i2);
auto input_t4 = predictor->GetInputTensor(input_names[3]);
input_t4->Reshape({run_batch, run_seq_len, 1});
input_t4->copy_from_cpu(i3);
ASSERT_TRUE(predictor->ZeroCopyRun());
auto output_names = predictor->GetOutputNames();
auto output_t = predictor->GetOutputTensor(output_names[0]);
std::vector<int> output_shape = output_t->shape();
int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
std::multiplies<int>());
out_data->resize(out_num);
output_t->copy_to_cpu(out_data->data());
}
void trt_ernie(bool with_fp16, std::vector<float> result) {
AnalysisConfig config;
std::string model_dir = FLAGS_infer_model;
SetConfig(&config, model_dir, true /* use_gpu */);
config.SwitchUseFeedFetchOps(false);
int head_number = 12;
int batch = 1;
int min_seq_len = 1;
int max_seq_len = 128;
int opt_seq_len = 128;
std::vector<int> min_shape = {batch, min_seq_len, 1};
std::vector<int> max_shape = {batch, max_seq_len, 1};
std::vector<int> opt_shape = {batch, opt_seq_len, 1};
// Set the input's min, max, opt shape
std::map<std::string, std::vector<int>> min_input_shape = {
{"read_file_0.tmp_0", min_shape},
{"read_file_0.tmp_1", min_shape},
{"read_file_0.tmp_2", min_shape},
{"stack_0.tmp_0", {batch, head_number, min_seq_len, min_seq_len}}};
std::map<std::string, std::vector<int>> max_input_shape = {
{"read_file_0.tmp_0", max_shape},
{"read_file_0.tmp_1", max_shape},
{"read_file_0.tmp_2", max_shape},
{"stack_0.tmp_0", {batch, head_number, max_seq_len, max_seq_len}}};
std::map<std::string, std::vector<int>> opt_input_shape = {
{"read_file_0.tmp_0", opt_shape},
{"read_file_0.tmp_1", opt_shape},
{"read_file_0.tmp_2", opt_shape},
{"stack_0.tmp_0", {batch, head_number, opt_seq_len, opt_seq_len}}};
auto precision = AnalysisConfig::Precision::kFloat32;
if (with_fp16) {
precision = AnalysisConfig::Precision::kHalf;
}
config.EnableTensorRtEngine(1 << 30, 1, 5, precision, true, false);
config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
opt_input_shape);
std::vector<float> out_data;
run(config, &out_data);
for (size_t i = 0; i < out_data.size(); i++) {
EXPECT_NEAR(result[i], out_data[i], 1e-6);
}
}
TEST(AnalysisPredictor, no_fp16) {
std::vector<float> result = {0.597841, 0.219972, 0.182187};
trt_ernie(false, result);
}
TEST(AnalysisPredictor, fp16) {
#ifdef SUPPORTS_CUDA_FP16
std::vector<float> result = {0.598336, 0.219558, 0.182106};
trt_ernie(true, result);
#endif
}
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/api/trt_dynamic_shape_ernie_test.cc
浏览文件 @ 1731b32d
......@@ -120,7 +120,7 @@ void trt_ernie(bool with_fp16, std::vector<float> result) {
if (with_fp16) {
precision = AnalysisConfig::Precision::kHalf;
}
config.EnableTensorRtEngine(1 << 30, 1, 5, precision, false, true);
config.EnableTensorRtEngine(1 << 30, 1, 5, precision, false, false);
config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
opt_input_shape);
std::vector<float> out_data;
......
paddle/fluid/operators/clip_op.h
浏览文件 @ 1731b32d
......@@ -25,17 +25,23 @@ namespace operators {
using framework::Tensor;
using platform::Transform;
#ifdef __NVCC__
template <typename T, typename UnaryOperation>
__global__ void ClipCudaKernel(const T* input, T* out, int num,
UnaryOperation op) {
int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx < num) {
out[idx] = op(input[idx]);
}
}
#endif
template <typename T>
class ClipFunctor {
public:
explicit ClipFunctor(const T min, const T max) : min_(min), max_(max) {}
HOSTDEVICE T operator()(const T& x) const {
if (x < min_)
return min_;
else if (x > max_)
return max_;
else
return x;
return x < min_ ? min_ : x > max_ ? max_ : x;
}
private:
......@@ -97,9 +103,20 @@ class ClipKernel : public framework::OpKernel<T> {
T* out_data = out->mutable_data<T>(context.GetPlace());
const T* x_data = x->data<T>();
int64_t numel = x->numel();
Transform<DeviceContext> trans;
trans(context.template device_context<DeviceContext>(), x_data,
x_data + numel, out_data, ClipFunctor<T>(min, max));
if (platform::is_gpu_place(context.GetPlace())) {
#ifdef __NVCC__
int threads = 256;
int blocks = (numel + threads - 1) / threads;
ClipCudaKernel<T, ClipFunctor<T>><<<
blocks, threads, 0,
context.template device_context<platform::CUDADeviceContext>()
.stream()>>>(x_data, out_data, numel, ClipFunctor<T>(min, max));
#endif
} else {
Transform<DeviceContext> trans;
trans(context.template device_context<DeviceContext>(), x_data,
x_data + numel, out_data, ClipFunctor<T>(min, max));
}
} else if (x_var->IsType<framework::SelectedRows>()) {
auto* x = context.Input<framework::SelectedRows>("X");
auto* out = context.Output<framework::SelectedRows>("Out");
......
paddle/fluid/operators/controlflow/compare_op.cc
浏览文件 @ 1731b32d
......@@ -13,8 +13,11 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/controlflow/compare_op.h"
#include <algorithm>
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
namespace paddle {
namespace operators {
......@@ -85,14 +88,22 @@ class CompareOp : public framework::OperatorWithKernel {
auto dim_x = context->GetInputDim("X");
auto dim_y = context->GetInputDim("Y");
PADDLE_ENFORCE_GE(dim_x.size(), dim_y.size(),
platform::errors::InvalidArgument(
"The size of dim_y should not be greater than "
"dim_x's, but received dim_y: %d > dim_x: %d.\n",
dim_y.size(), dim_x.size()));
context->SetOutputDim("Out", context->GetInputDim("X"));
context->ShareLoD("X", "Out");
if (context->GetInputDim("X") == context->GetInputDim("Y")) {
context->ShareDim("X", /*->*/ "Out");
context->ShareLoD("X", /*->*/ "Out");
} else {
int max_dim = std::max(dim_x.size(), dim_y.size());
int axis = std::abs(dim_x.size() - dim_y.size());
std::vector<int> x_dims_array(max_dim);
std::vector<int> y_dims_array(max_dim);
std::vector<int> out_dims_array(max_dim);
GetBroadcastDimsArrays(dim_x, dim_y, x_dims_array.data(),
y_dims_array.data(), out_dims_array.data(),
max_dim, axis);
context->SetOutputDim("Out", framework::make_ddim(out_dims_array));
// to do
context->ShareLoD("X", /*->*/ "Out");
}
}
framework::OpKernelType GetExpectedKernelType(
......
paddle/fluid/operators/conv_cudnn_helper.h
浏览文件 @ 1731b32d
......@@ -162,19 +162,7 @@ struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
workspace_size = GetWorkspaceSize(args, algo);
if (workspace_size > workspace_size_limit) {
has_got_workspace_size = false;
VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
"the workspace size request("
<< workspace_size << ") exceeds the limit("
<< workspace_size_limit << ")";
}
if (!has_got_workspace_size) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardAlgorithm(
args.handle, args.idesc.desc(), args.wdesc.desc(),
args.cdesc.desc(), args.odesc.desc(),
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
workspace_size_limit = workspace_size;
}
#else
PADDLE_ENFORCE_CUDA_SUCCESS(
......@@ -303,19 +291,8 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
#endif
workspace_size = GetWorkspaceSize(args, algo);
if (workspace_size > workspace_size_limit) {
workspace_size_limit = workspace_size;
has_got_workspace_size = false;
VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
"the workspace size request("
<< workspace_size << ") exceeds the limit("
<< workspace_size_limit << ")";
}
if (!has_got_workspace_size) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
args.handle, args.wdesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.idesc.desc(),
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
}
#else
PADDLE_ENFORCE_CUDA_SUCCESS(
......@@ -432,19 +409,7 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
algo = (perf_results.get())[best_algo_idx].algo;
workspace_size = GetWorkspaceSize(args, algo);
if (workspace_size > workspace_size_limit) {
has_got_workspace_size = false;
VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
"the workspace size request("
<< workspace_size << ") exceeds the limit("
<< workspace_size_limit << ")";
}
if (!has_got_workspace_size) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
args.handle, args.idesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.wdesc.desc(),
CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
workspace_size = workspace_size_limit;
}
#else
PADDLE_ENFORCE_CUDA_SUCCESS(
......
paddle/fluid/operators/elementwise/elementwise_op_function.h
浏览文件 @ 1731b32d
......@@ -197,6 +197,40 @@ void CommonForwardBroadcastCPU(const framework::Tensor *x,
}
#ifdef __NVCC__
template <typename Functor, typename T, typename OutType>
__global__ void ElementwiseKernel(const T *x, const T *y, OutType *out, int pre,
int n, int post, int total, Functor func) {
int tid = threadIdx.x + blockDim.x * blockIdx.x;
int idx = tid / post % n;
if (tid < total) {
out[tid] = func(x[tid], y[idx]);
}
}
template <typename Functor, typename T, typename OutType>
void ComputeElementwiseCUDA(const framework::Tensor *x,
const framework::Tensor *y, framework::Tensor *z,
int pre, int n, int post,
const platform::CUDADeviceContext &ctx,
Functor func, const bool is_xsize_larger = true) {
const T *x_data = x->data<T>();
const T *y_data = y->data<T>();
OutType *out_data = z->mutable_data<OutType>(ctx.GetPlace());
int numel = pre * n * post;
int threads = 256;
int blocks = (numel + threads - 1) / threads;
if (is_xsize_larger) {
ElementwiseKernel<Functor, T,
OutType><<<blocks, threads, 0, ctx.stream()>>>(
x_data, y_data, out_data, pre, n, post, numel, func);
} else {
ElementwiseKernel<Functor, T,
OutType><<<blocks, threads, 0, ctx.stream()>>>(
y_data, x_data, out_data, pre, n, post, numel, func);
}
}
template <typename Functor, typename T, typename OutType = T>
__global__ void CommonForwardBroadcastCUDAKernel(
const int *x_strides_array, const int *y_strides_array,
......@@ -1908,6 +1942,16 @@ void ElementwiseComputeEx(const framework::ExecutionContext &ctx,
ctx, x, y, z, x_dims, y_dims, func, axis, is_xsize_larger);
return;
}
if (platform::is_gpu_place(ctx.GetPlace())) {
#ifdef __NVCC__
ComputeElementwiseCUDA<Functor, T, OutType>(
x, y, z, pre, n, post,
ctx.template device_context<platform::CUDADeviceContext>(), func,
is_xsize_larger);
#endif
return;
}
if (post == 1) {
functor.RunRowWise(n, pre);
return;
......
paddle/fluid/operators/fused/conv_fusion_op.cu
浏览文件 @ 1731b32d
......@@ -204,11 +204,17 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
auto x_dims = framework::vectorize(transformed_input.dims());
auto f_dims = framework::vectorize(filter->dims());
if (!exhaustive_search) {
int perf_count;
int best_algo_idx = 0;
size_t tmp_size = 0;
std::unique_ptr<cudnnConvolutionFwdAlgoPerf_t[]> perf_results(
new cudnnConvolutionFwdAlgoPerf_t[kNUM_CUDNN_FWD_ALGS]);
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardAlgorithm(
platform::dynload::cudnnGetConvolutionForwardAlgorithm_v7(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
cudnn_output_desc, kNUM_CUDNN_FWD_ALGS, &perf_count,
perf_results.get()));
algo = (perf_results.get())[best_algo_idx].algo;
VLOG(3) << "cuDNN forward algo " << algo;
} else {
std::function<cudnnConvolutionFwdAlgo_t()> search_func =
......
paddle/fluid/operators/fused/fusion_conv_inception_op.cu
浏览文件 @ 1731b32d
......@@ -179,16 +179,23 @@ class CUDNNConvInceptionFusionOpKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
out_desc[i], cudnn_dtype, 4, out_dims[i].data(),
out_strides[i].data()));
int perf_count;
int best_algo_idx = 0;
size_t tmp_size = 0;
std::unique_ptr<cudnnConvolutionFwdAlgoPerf_t[]> perf_results(
new cudnnConvolutionFwdAlgoPerf_t[kNUM_CUDNN_FWD_ALGS]);
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardAlgorithm(
platform::dynload::cudnnGetConvolutionForwardAlgorithm_v7(
handle, in_desc[i], filter_desc[i], conv_desc[i], out_desc[i],
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo[i]));
size_t tmp_size = 0;
kNUM_CUDNN_FWD_ALGS, &perf_count, perf_results.get()));
algo[i] = (perf_results.get())[best_algo_idx].algo;
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, in_desc[i], filter_desc[i], conv_desc[i], out_desc[i],
algo[i], &tmp_size));
workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
}
cudnnActivationDescriptor_t cudnn_act_desc =
......
paddle/fluid/operators/math/bert_encoder_functor.cu
浏览文件 @ 1731b32d
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <cuda_runtime.h>
#include <algorithm>
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/math/bert_encoder_functor.h"
......
paddle/fluid/platform/dynload/cudnn.h
浏览文件 @ 1731b32d
......@@ -54,7 +54,6 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
__macro(cudnnSetTensorNdDescriptor); \
__macro(cudnnGetTensorNdDescriptor); \
__macro(cudnnGetConvolutionNdForwardOutputDim); \
__macro(cudnnGetConvolutionForwardAlgorithm); \
__macro(cudnnCreateTensorDescriptor); \
__macro(cudnnDestroyTensorDescriptor); \
__macro(cudnnCreateFilterDescriptor); \
......@@ -102,7 +101,6 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
__macro(cudnnDropoutGetStatesSize); \
__macro(cudnnSetDropoutDescriptor); \
__macro(cudnnCreateRNNDescriptor); \
__macro(cudnnSetRNNDescriptor); \
__macro(cudnnGetRNNParamsSize); \
__macro(cudnnGetRNNWorkspaceSize); \
__macro(cudnnGetRNNTrainingReserveSize); \
......@@ -126,12 +124,19 @@ CUDNN_DNN_ROUTINE_EACH_R2(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
#if CUDNN_VERSION >= 3000
#define CUDNN_DNN_ROUTINE_EACH_AFTER_R3(__macro) \
__macro(cudnnGetConvolutionBackwardFilterWorkspaceSize); \
__macro(cudnnGetConvolutionBackwardDataAlgorithm); \
__macro(cudnnGetConvolutionBackwardFilterAlgorithm); \
__macro(cudnnGetConvolutionBackwardDataWorkspaceSize);
CUDNN_DNN_ROUTINE_EACH_AFTER_R3(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
#endif
// APIs available after R3:
#if CUDNN_VERSION >= 3000 && CUDNN_VERSION < 8000
#define CUDNN_DNN_ROUTINE_EACH_AFTER_R3_LESS_R8(__macro) \
__macro(cudnnGetConvolutionBackwardFilterAlgorithm); \
__macro(cudnnGetConvolutionForwardAlgorithm); \
__macro(cudnnSetRNNDescriptor);
CUDNN_DNN_ROUTINE_EACH_AFTER_R3_LESS_R8(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
#endif
// APIs available after R4:
#if CUDNN_VERSION >= 4007
#define CUDNN_DNN_ROUTINE_EACH_AFTER_R4(__macro) \
......@@ -183,6 +188,12 @@ CUDNN_DNN_ROUTINE_EACH_R7(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
__macro(cudnnGetBatchNormalizationTrainingExReserveSpaceSize);
CUDNN_DNN_ROUTINE_EACH_AFTER_R7(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
#endif
#if CUDNN_VERSION >= 8000
#define CUDNN_DNN_ROUTINE_EACH_R8(__macro) __macro(cudnnSetRNNDescriptor_v8);
CUDNN_DNN_ROUTINE_EACH_R8(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
#endif
} // namespace dynload
} // namespace platform
} // namespace paddle
paddle/fluid/platform/dynload/tensorrt.cc
浏览文件 @ 1731b32d
......@@ -13,18 +13,62 @@
limitations under the License. */
#include "paddle/fluid/platform/dynload/tensorrt.h"
#include <string>
namespace paddle {
namespace platform {
namespace dynload {
std::once_flag tensorrt_dso_flag;
void *tensorrt_dso_handle;
void* tensorrt_dso_handle;
#define DEFINE_WRAP(__name) DynLoad__##__name __name
TENSORRT_RAND_ROUTINE_EACH(DEFINE_WRAP);
void* GetTensorRtHandle() {
#if defined(__APPLE__) || defined(__OSX__)
std::string dso_name = "libnvinfer.dylib";
#elif defined(_WIN32)
std::string dso_name = "nvinfer.dll";
#else
std::string dso_name = "libnvinfer.so";
#endif
#if !defined(_WIN32)
int dynload_flags = RTLD_LAZY | RTLD_LOCAL;
#else
int dynload_flags = 0;
#endif // !_WIN32
void* dso_handle = dlopen(dso_name.c_str(), dynload_flags);
if (nullptr == dso_handle) {
auto error_msg =
"TensorRT dynamic library (%s) that Paddle depends on is not "
"configured correctly. (error code is %s)\n"
" Suggestions:\n"
" 1. Check if TensorRT "
"is installed correctly and its version is matched with paddlepaddle "
"you installed.\n"
" 2. Configure TensorRT dynamic library environment variables as "
"follows:\n"
" - Linux: set LD_LIBRARY_PATH by `export LD_LIBRARY_PATH=...`\n"
" - Windows: set PATH by `set PATH=XXX;%PATH%`\n"
" - Mac: set DYLD_LIBRARY_PATH by `export DYLD_LIBRARY_PATH=...` "
"[Note: After Mac OS 10.11, using the DYLD_LIBRARY_PATH is "
"impossible unless System Integrity Protection (SIP) is disabled.]";
#if !defined(_WIN32)
auto errorno = dlerror();
#else
auto errorno = GetLastError();
#endif // !_WIN32
std::cerr << string::Sprintf(error_msg, dso_name, errorno);
}
return dso_handle;
}
} // namespace dynload
} // namespace platform
} // namespace paddle
paddle/fluid/platform/dynload/tensorrt.h
浏览文件 @ 1731b32d
......@@ -27,6 +27,8 @@ namespace paddle {
namespace platform {
namespace dynload {
void* GetTensorRtHandle();
extern std::once_flag tensorrt_dso_flag;
extern void* tensorrt_dso_handle;
......@@ -36,8 +38,7 @@ extern void* tensorrt_dso_handle;
auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) { \
using tensorrt_func = decltype(&::__name); \
std::call_once(tensorrt_dso_flag, []() { \
tensorrt_dso_handle = \
paddle::platform::dynload::GetTensorRtDsoHandle(); \
tensorrt_dso_handle = paddle::platform::dynload::GetTensorRtHandle(); \
PADDLE_ENFORCE_NOT_NULL(tensorrt_dso_handle, \
platform::errors::Unavailable( \
"Load tensorrt %s failed", #__name)); \
......@@ -53,7 +54,8 @@ extern void* tensorrt_dso_handle;
#define TENSORRT_RAND_ROUTINE_EACH(__macro) \
__macro(createInferBuilder_INTERNAL); \
__macro(createInferRuntime_INTERNAL);
__macro(createInferRuntime_INTERNAL); \
__macro(getPluginRegistry);
TENSORRT_RAND_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_TENSORRT_WRAP)
......
paddle/scripts/paddle_build.sh
浏览文件 @ 1731b32d
......@@ -958,6 +958,7 @@ function parallel_test() {
ut_total_startTime_s=`date +%s`
mkdir -p ${PADDLE_ROOT}/build
cd ${PADDLE_ROOT}/build
pip install ${PADDLE_ROOT}/build/python/dist/*whl
if [ "$WITH_GPU" == "ON" ];then
parallel_test_base_gpu
else
......
python/paddle/fleet/base/fleet_base.py
浏览文件 @ 1731b32d
......@@ -279,8 +279,11 @@ class Fleet(object):
# for more examples, please reference https://github.com/PaddlePaddle/Fleet
"""
context = {}
# cache original feed forward program
self.origin_main_program = loss.block.program
context["origin_main_program"] = self.origin_main_program
context["loss"] = loss
if startup_program == None:
self.origin_startup_program = \
paddle.default_startup_program().clone(for_test=False)
......@@ -288,6 +291,8 @@ class Fleet(object):
else:
self.origin_startup_program = \
startup_program.clone(for_test=False)
context["origin_startup_program"] = startup_program
context["role_maker"] = self._role_maker
# compile time
distributed_optimizer_list = \
......@@ -317,6 +322,9 @@ class Fleet(object):
valid_strategy = self.strategy_compiler._get_valid_strategy(
self.user_defined_strategy, can_not_apply_optimizer_list)
context["valid_strategy"] = valid_strategy
self.valid_strategy = valid_strategy
optimize_ops = []
......@@ -334,6 +342,8 @@ class Fleet(object):
parameter_list=parameter_list,
no_grad_set=no_grad_set)
context["program_optimize_ops"] = optimize_ops
context["program_params_grads"] = params_grads
if graph_optimizer:
optimize_ops, params_grads = graph_optimizer.minimize(
loss,
......@@ -344,12 +354,13 @@ class Fleet(object):
# if a graph optimizer takes effect, mostly
# optimizers_ops and params_grads are None
# i.e. users can not modify current computation graph anymore
context["graph_optimize_ops"] = optimize_ops
context["graph_optimize_grads"] = params_grads
if self._runtime_handle is None:
self._runtime_handle = RuntimeFactory()._create_runtime(
valid_strategy, self._role_maker, optimize_ops, params_grads)
self._runtime_handle = RuntimeFactory()._create_runtime(context)
if self._util is None:
self._util = UtilFactory()._create_util(
valid_strategy, self._role_maker, optimize_ops, params_grads)
self._util = UtilFactory()._create_util(context)
return optimize_ops, params_grads
python/paddle/fleet/base/runtime_factory.py
浏览文件 @ 1731b32d
......@@ -18,10 +18,8 @@ class RuntimeFactory(object):
def __init__(self):
pass
def _create_runtime(self, final_dist_strategy, role_maker, opt_ops,
params_grads):
if role_maker._is_collective:
def _create_runtime(self, context):
if context["role_maker"]._is_collective:
collective_runtime = CollectiveRuntime()
collective_runtime._set_basic_info(final_dist_strategy, role_maker,
opt_ops, params_grads)
collective_runtime._set_basic_info(context)
return collective_runtime
python/paddle/fleet/base/util_factory.py
浏览文件 @ 1731b32d
......@@ -20,11 +20,10 @@ __all__ = ['UtilBase']
class UtilFactory(object):
def _create_util(self, dist_strategy, role_maker, optimize_ops,
params_grads):
def _create_util(self, context):
util = UtilBase()
util._set_strategy(dist_strategy)
util._set_role_maker(role_maker)
util._set_strategy(context["valid_strategy"])
util._set_role_maker(context["role_maker"])
return util
......
python/paddle/fleet/cloud_utils.py 0 → 100644
浏览文件 @ 1731b32d
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import paddle
from paddle.fleet.launch_utils import get_cluster, logger
def get_cloud_cluster(args_node_ips, selected_gpus, args_port=6170):
"""
args_node_ips, args_node_ip:string
"""
#you can automatically get ip info while using paddlecloud multi nodes mode.
node_ips = os.getenv("PADDLE_TRAINERS")
assert node_ips is not None, "PADDLE_TRAINERS should not be None"
node_ip = os.getenv("POD_IP")
assert node_ip is not None, "POD_IP should not be None"
node_rank = os.getenv("PADDLE_TRAINER_ID")
assert node_rank is not None, "PADDLE_TRAINER_ID should not be None"
node_ips = node_ips.split(",")
num_nodes = len(node_ips)
node_rank = int(node_rank)
if args_node_ips != "127.0.0.1" and args_node_ips != ",".join(node_ips):
logger.warning(
"Please NOTE: When using paddlecloud, cluster_node_ips is \
automatically got from PADDLE_TRAINERS(multi nodes) or POD_IP(single node).\
Your input cluster_node_ips: {} doesn't equals to IPs: {} from \
paddlecloud environment.".format(args_node_ips, node_ips))
started_port = args_port
print("num_nodes:", num_nodes)
if num_nodes > 1:
try:
paddle_port = int(os.getenv("PADDLE_PORT", ""))
paddle_port_num = int(os.getenv("TRAINER_PORTS_NUM", ""))
if paddle_port_num >= len(
selected_gpus) and paddle_port != args_port:
logger.warning("Use Cloud specified port:{}.".format(
paddle_port))
started_port = paddle_port
except Exception as e:
print(e)
pass
if started_port is None:
started_port = 6170
logger.debug("parsed from args:node_ips:{} \
node_ip:{} node_rank:{} started_port:{}"
.format(node_ips, node_ip, node_rank, started_port))
ports = [x for x in range(started_port, started_port + len(selected_gpus))]
cluster, pod = get_cluster(node_ips, node_ip, ports, selected_gpus)
return cluster, cluster.pods[node_rank]
def use_paddlecloud():
node_ips = os.getenv("PADDLE_TRAINERS")
node_ip = os.getenv("POD_IP")
node_rank = os.getenv("PADDLE_TRAINER_ID")
if node_ips is None or node_ip is None or node_rank is None:
return False
else:
return True
def get_trainers_num():
return int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
python/paddle/fleet/launch.py 0 → 100644
浏览文件 @ 1731b32d
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
paddle.distributed.launch is a module that spawns multiple distributed
process on each training node for gpu training and cpu training.
Usage:
In both of single node training or multiple node training, this module
launch a process on each of the given gpu card or cpu machine.
GPU training:
1. for single node training with all visible gpu cards:
fleetrun your_training_py (arg1 arg2 and all others)
2. for single node training with [0,4) cards
fleetrun --gpus="0,1,2,3" your_training_py (arg1 arg2 and all others)
3. for multiple node training such as two node:192.168.0.16, 192.168.0.17
on 192.168.0.16:
fleetrun --ips="192.168.0.16,192.168.0.17" --node_ip=192.168.0.16 \
your_training_py (arg1 arg2 and all others)
on 192.168.0.17:
fleetrun --ips="192.168.0.16,192.168.0.17" \
--node_ip=192.168.0.17 \
your_training_py (arg1 arg2 and all others)
CPU training:
1. for single node training with multi servers and workers:
fleetrun --server_num=1 --worker_num=4 your_training_py (arg1 arg2 and all others)
2. for multiple node training such as two node:192.168.0.16, 192.168.0.17 \
with 2 servers and 4 workers.
on 192.168.0.16:
fleetrun --servers="192.168.0.16:6170,192.168.0.17:6171" \
--workers="192.168.0.16:6172,192.168.0.17:6173,192.168.0.16:6174,192.168.0.17:6175" \
your_training_py (arg1 arg2 and all others)
on 192.168.0.17:
fleetrun --servers="192.168.0.16:6170,192.168.0.17:6171" \
--workers="192.168.0.16:6172,192.168.0.17:6173,192.168.0.16:6174,192.168.0.17:6175" \
your_training_py (arg1 arg2 and all others)
"""
from __future__ import print_function
import sys
from sys import version
import subprocess
import os
import time
import six
import copy
from argparse import ArgumentParser, REMAINDER
import paddle
import paddle.fluid as fluid
from paddle.fleet.launch_utils import *
import paddle.fleet.cloud_utils as cloud_utils
def _print_arguments(args):
print("----------- Configuration Arguments -----------")
for arg, value in sorted(six.iteritems(vars(args))):
print("%s: %s" % (arg, value))
print("------------------------------------------------")
def _parse_args():
"""
Helper function parsing the command line options
@retval ArgumentParser
"""
parser = ArgumentParser(
description='''start paddle training using multi-process mode.
see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2-
''')
#Optional arguments for the launch helper
parser.add_argument(
"--ips",
type=str,
default="127.0.0.1",
help="Paddle cluster nodes ips, such as 192.168.0.16,192.168.0.17..")
parser.add_argument(
"--gpus",
type=str,
default=None,
help="It's for gpu training and the training process will run on the gpus,"
"each process is bound to a single GPU. And if it's not set, this module will use all the gpu cards for training."
)
parser.add_argument(
"--servers", type=str, default="", help="User defined servers ip:port")
parser.add_argument(
"--workers", type=str, default="", help="User defined workers ip:port")
parser.add_argument(
"--worker_num", type=int, default=2, help="number of workers")
parser.add_argument(
"--server_num", type=int, default=2, help="number of servers")
parser.add_argument(
"--log_dir",
type=str,
help="The path for each process's log.If it's not set, the log will printed to default pipe."
)
#positional
parser.add_argument(
"training_script",
type=str,
help="The full path to the single GPU training "
"program/script to be launched in parallel, "
"followed by all the arguments for the "
"training script")
#rest from the training program
parser.add_argument('training_script_args', nargs=REMAINDER)
return parser.parse_args()
def get_cluster_from_args(args, gpus):
node_ips = [x.strip() for x in args.ips.split(',')]
if len(node_ips) == 1:
node_ip = node_ips[0]
else:
_, node_ip = get_host_name_ip()
# node_ip = args.node_ip
assert node_ip in node_ips, "Can't find your local ip {%s} in node_ips:{%s}" \
% (node_ip, node_ips)
node_rank = node_ips.index(node_ip)
logger.debug("parsed from args:node_ips:{} node_ip:{} node_rank:{}".format(
node_ips, node_ip, node_rank))
free_ports = None
if not cloud_utils.use_paddlecloud() and len(
node_ips) <= 1 and os.environ.get('FLAGS_START_PORT') is None:
free_ports = find_free_ports(len(gpus))
if free_ports is not None:
free_ports = list(free_ports)
else:
start_port = 6070
if os.environ.get('FLAGS_START_PORT') is not None:
start_port = os.environ.get('FLAGS_START_PORT')
free_ports = [x for x in range(start_port, start_port + len(gpus))]
return get_cluster(node_ips, node_ip, free_ports, gpus)
def get_gpus(gpus):
if gpus is None:
gpus_num = fluid.core.get_cuda_device_count()
gpus = [str(x) for x in range(0, gpus_num)]
else:
cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
if cuda_visible_devices is None or cuda_visible_devices == "":
gpus = [x.strip() for x in gpus.split(',')]
else:
# change gpus into relative values
# e.g. CUDA_VISIBLE_DEVICES=4,5,6,7; args.gpus=4,5,6,7;
# therefore gpus=0,1,2,3
cuda_visible_devices_list = cuda_visible_devices.split(',')
for x in gpus.split(','):
assert x in cuda_visible_devices_list, "Can't find "\
"your gpus %s in CUDA_VISIBLE_DEVICES[%s]."\
% (x, cuda_visible_devices)
gpus = [
cuda_visible_devices_list.index(x.strip())
for x in gpus.split(',')
]
return gpus
def launch_collective(args):
# parse arguments, used for cloud-single-machine and local
gpus = get_gpus(args.gpus)
trainers_num = cloud_utils.get_trainers_num()
logger.debug("parsed from args trainerss_num:{} gpus:{}".format(
trainers_num, gpus))
cluster = None
pod = None
if cloud_utils.use_paddlecloud() and trainers_num != 1:
cluster, pod = cloud_utils.get_cloud_cluster(args.ips, gpus)
logger.info("get cluster from cloud:{}".format(cluster))
else:
# trainers_num = 1 or not use paddlecloud ips="a,b"
cluster, pod = get_cluster_from_args(args, gpus)
logger.info("get cluster from args:{}".format(cluster))
procs = start_local_trainers(
cluster,
pod,
training_script=args.training_script,
training_script_args=args.training_script_args,
log_dir=args.log_dir)
while True:
alive = watch_local_trainers(procs, cluster.trainers_nranks())
if not alive:
logger.info("Local procs complete, POD info:{}".format(pod))
break
time.sleep(3)
def launch_ps(args):
worker_num = args.worker_num
server_num = args.server_num
start_port = 6170
if os.environ.get('FLAGS_START_PORT') is not None:
start_port = os.environ.get('FLAGS_START_PORT')
default_env = os.environ.copy()
current_env = copy.copy(default_env)
current_env.pop("http_proxy", None)
current_env.pop("https_proxy", None)
procs = []
cmds = []
log_fns = []
ports = range(start_port, start_port + server_num, 1)
default_endpoints = ",".join(["127.0.0.1:" + str(x) for x in ports])
user_endpoints = ""
if args.servers == "":
user_endpoints = default_endpoints
else:
user_endpoints = args.servers
user_endpoints_ips = [x.split(":")[0] for x in user_endpoints.split(",")]
user_endpoints_port = [x.split(":")[1] for x in user_endpoints.split(",")]
for i in range(server_num):
current_env.update({
"PADDLE_PSERVERS_IP_PORT_LIST": user_endpoints,
"PADDLE_PORT": user_endpoints_port[i],
"TRAINING_ROLE": "PSERVER",
"PADDLE_TRAINERS_NUM": str(worker_num),
"POD_IP": user_endpoints_ips[i]
})
cmd = [sys.executable, "-u", args.training_script
] + args.training_script_args
cmds.append(cmd)
if args.log_dir is not None:
os.system("mkdir -p {}".format(args.log_dir))
fn = open("%s/serverlog.%d" % (args.log_dir, i), "w")
log_fns.append(fn)
proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
else:
proc = subprocess.Popen(cmd, env=current_env)
procs.append(proc)
for i in range(worker_num):
current_env.update({
"PADDLE_PSERVERS_IP_PORT_LIST": user_endpoints,
"PADDLE_TRAINERS_NUM": str(worker_num),
"TRAINING_ROLE": "TRAINER",
"PADDLE_TRAINER_ID": str(i)
})
cmd = [sys.executable, "-u", args.training_script
] + args.training_script_args
cmds.append(cmd)
if args.log_dir is not None:
os.system("mkdir -p {}".format(args.log_dir))
fn = open("%s/workerlog.%d" % (args.log_dir, i), "w")
log_fns.append(fn)
proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
else:
proc = subprocess.Popen(cmd, env=current_env)
procs.append(proc)
# only wait worker to finish here
for i, proc in enumerate(procs):
if i < server_num:
continue
procs[i].wait()
if len(log_fns) > 0:
log_fns[i].close()
print("all workers exit, going to finish parameter server", file=sys.stderr)
for i in range(server_num):
if len(log_fns) > 0:
log_fns[i].close()
procs[i].terminate()
print("all parameter server are killed", file=sys.stderr)
def launch():
args = _parse_args()
logger = get_logger()
_print_arguments(args)
ps_args = ['--worker_num', '--server_num', '--servers', '--workers']
collective_args = ['--ips', '--gpus']
has_ps_args = [
ps_arg for ps_arg in ps_args if ps_arg in " ".join(sys.argv[1:-1])
]
has_collective_args = [
co_arg for co_arg in collective_args
if co_arg in " ".join(sys.argv[1:-1])
]
if len(has_ps_args) > 0 or fluid.core.get_cuda_device_count() == 0:
logger.info("Run cpu parameter-sever mode.")
launch_ps(args)
elif len(has_collective_args) > 0:
logger.info("Run gpu collective mode.")
launch_collective(args)
else:
logger.warning(
"Not found distinct args. Default use gpu collective mode")
launch_collective(args)
if __name__ == "__main__":
launch()
python/paddle/fleet/launch_utils.py 0 → 100644
浏览文件 @ 1731b32d
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import logging
import socket
import time
import os
import signal
import copy
import sys
import subprocess
from contextlib import closing
import socket
logger = logging.getLogger("root")
logger.propagate = False
class Cluster(object):
def __init__(self, hdfs):
self.job_server = None
self.pods = []
self.hdfs = None
self.job_stage_flag = None
def __str__(self):
return "job_server:{} pods:{} job_stage_flag:{} hdfs:{}".format(
self.job_server, [str(pod) for pod in self.pods],
self.job_stage_flag, self.hdfs)
def __eq__(self, cluster):
if len(self.pods) != len(cluster.pods):
return False
for a, b in zip(self.pods, cluster.pods):
if a != b:
return False
if self.job_stage_flag != cluster.job_stage_flag:
return False
return True
def __ne__(self, cluster):
return not self.__eq__(cluster)
def update_pods(cluster):
self.pods = copy.copy(cluster.pods)
def trainers_nranks(self):
return len(self.trainers_endpoints())
def pods_nranks(self):
return len(self.pods)
def trainers_endpoints(self):
r = []
for pod in self.pods:
for t in pod.trainers:
r.append(t.endpoint)
return r
def pods_endpoints(self):
r = []
for pod in self.pods:
ep = "{}:{}".format(pod.addr, pod.port)
assert pod.port != None and pod.addr != None, "{} not a valid endpoint".format(
ep)
r.append(ep)
return r
def get_pod_by_id(self, pod_id):
for pod in self.pods:
if str(pod_id) == str(pod.id):
return pod
return None
class JobServer(object):
def __init__(self):
self.endpoint = None
def __str__(self):
return "{}".format(self.endpoint)
def __eq__(self, j):
return self.endpint == j.endpoint
def __ne__(self, j):
return not self == j
class Trainer(object):
def __init__(self):
self.gpus = []
self.endpoint = None
self.rank = None
def __str__(self):
return "gpu:{} endpoint:{} rank:{}".format(self.gpus, self.endpoint,
self.rank)
def __eq__(self, t):
if len(self.gpus) != len(t.gpus):
return False
if self.endpoint != t.endpoint or \
self.rank != t.rank:
return False
for a, b in zip(self.gpus, t.gpus):
if a != b:
return False
return True
def __ne__(self, t):
return not self == t
def rank(self):
return self.rank
class Pod(object):
def __init__(self):
self.rank = None
self.id = None
self.addr = None
self.port = None
self.trainers = []
self.gpus = []
def __str__(self):
return "rank:{} id:{} addr:{} port:{} visible_gpu:{} trainers:{}".format(
self.rank, self.id, self.addr, self.port, self.gpus,
[str(t) for t in self.trainers])
def __eq__(self, pod):
if self.rank != pod.rank or \
self.id != pod.id or \
self.addr != pod.addr or \
self.port != pod.port:
logger.debug("pod {} != pod".format(self, pod))
return False
if len(self.trainers) != len(pod.trainers):
logger.debug("trainers {} != {}".format(self.trainers,
pod.trainers))
return False
for i in range(len(self.trainers)):
if self.trainers[i] != pod.trainers[i]:
logger.debug("trainer {} != {}".format(self.trainers[i],
pod.trainers[i]))
return False
return True
def __ne__(self, pod):
return not self == pod
def parse_response(self, res_pods):
pass
def rank(self):
return self.rank
def get_visible_gpus(self):
r = ""
for g in self.gpus:
r += "{},".format(g)
assert r != "", "this pod {} can't see any gpus".format(self)
r = r[:-1]
return r
def get_logger(log_level=20, name="root"):
logger = logging.getLogger(name)
logger.setLevel(log_level)
log_handler = logging.StreamHandler()
log_format = logging.Formatter(
'%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s')
log_handler.setFormatter(log_format)
logger.addHandler(log_handler)
return logger
def get_cluster(node_ips, node_ip, paddle_ports, selected_gpus):
assert type(paddle_ports) is list, "paddle_ports must be list"
cluster = Cluster(hdfs=None)
trainer_rank = 0
for node_rank, ip in enumerate(node_ips):
pod = Pod()
pod.rank = node_rank
pod.addr = ip
for i in range(len(selected_gpus)):
trainer = Trainer()
trainer.gpus.append(selected_gpus[i])
trainer.endpoint = "%s:%d" % (ip, paddle_ports[i])
trainer.rank = trainer_rank
trainer_rank += 1
pod.trainers.append(trainer)
cluster.pods.append(pod)
pod_rank = node_ips.index(node_ip)
return cluster, cluster.pods[pod_rank]
def terminate_local_procs(procs):
for p in procs:
if p.proc.poll() is None:
p.proc.terminate()
p.log_fn.close()
logger.debug("terminate process id:{}".format(p.proc.pid))
#wait all process terminiated
time.sleep(3)
for step in range(0, 50):
alive = False
for p in procs:
if p.proc.poll() is None: # not termniate
os.kill(p.proc.pid, signal.SIGKILL)
alive = True
if not alive:
logger.info("terminate all the procs")
return
time.sleep(3)
logger.fatal("can't kill all process and exit")
exit(1)
def get_host_name_ip():
try:
host_name = socket.gethostname()
host_ip = socket.gethostbyname(host_name)
return host_name, host_ip
except:
return None
def add_arguments(argname, type, default, help, argparser, **kwargs):
"""Add argparse's argument.
Usage:
.. code-block:: python
parser = argparse.ArgumentParser()
add_argument("name", str, "Jonh", "User name.", parser)
args = parser.parse_args()
"""
type = distutils.util.strtobool if type == bool else type
argparser.add_argument(
"--" + argname,
default=default,
type=type,
help=help + ' Default: %(default)s.',
**kwargs)
def find_free_ports(num):
def __free_port():
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
s.bind(('', 0))
return s.getsockname()[1]
port_set = set()
step = 0
while True:
port = __free_port()
if port not in port_set:
port_set.add(port)
if len(port_set) >= num:
return port_set
step += 1
if step > 100:
print(
"can't find avilable port and use the specified static port now!"
)
return None
return None
class TrainerProc(object):
def __init__(self):
self.proc = None
self.log_fn = None
self.log_offset = None
self.rank = None
self.local_rank = None
self.cmd = None
def start_local_trainers(cluster,
pod,
training_script,
training_script_args,
log_dir=None):
current_env = copy.copy(os.environ.copy())
#paddle broadcast ncclUniqueId use socket, and
#proxy maybe make trainers unreachable, so delete them.
#if we set them to "", grpc will log error message "bad uri"
#so just delete them.
current_env.pop("http_proxy", None)
current_env.pop("https_proxy", None)
procs = []
for idx, t in enumerate(pod.trainers):
proc_env = {
"FLAGS_selected_gpus": "%s" % ",".join([str(g) for g in t.gpus]),
"PADDLE_TRAINER_ID": "%d" % t.rank,
"PADDLE_CURRENT_ENDPOINT": "%s" % t.endpoint,
"PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(),
"PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints())
}
current_env.update(proc_env)
logger.debug("trainer proc env:{}".format(current_env))
cmd = [sys.executable, "-u", training_script] + training_script_args
logger.info("start trainer proc:{} env:{}".format(cmd, proc_env))
fn = None
if log_dir is not None:
os.system("mkdir -p {}".format(log_dir))
fn = open("%s/workerlog.%d" % (log_dir, idx), "a")
proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
else:
proc = subprocess.Popen(cmd, env=current_env)
tp = TrainerProc()
tp.proc = proc
tp.rank = t.rank
tp.local_rank = idx
tp.log_fn = fn
tp.log_offset = fn.tell() if fn else None
tp.cmd = cmd
procs.append(tp)
return procs
def pull_worker_log(tp):
if tp.log_fn:
with open(tp.log_fn.name, 'r') as fin:
fin.seek(tp.log_offset, 0)
for line in fin:
try:
sys.stdout.write(line)
except UnicodeEncodeError:
sys.stdout.write(
'UnicodeEncodeError occurs at this line. '
'Please refer to the original log file "%s"\n' %
tp.log_fn.name)
tp.log_offset = fin.tell()
def watch_local_trainers(procs, nranks):
try:
error = False
error_rank = []
# wait all process finish or one error
alive = False
for p in procs:
if p.log_fn and p.local_rank == 0:
pull_worker_log(p)
ret = p.proc.poll()
if ret is None:
alive = True
elif ret != 0:
error = True
error_rank.append(p.rank)
if error:
terminate_local_procs(procs)
exit(1)
except KeyboardInterrupt:
logger.warning("KeyboardInterrupt, exit")
terminate_local_procs(procs)
raise
except SystemExit:
logger.error(
"ABORT!!! Out of all {} trainers, the trainer process with rank={} was aborted. Please check its log.".
format(nranks, error_rank))
terminate_local_procs(procs)
raise
except:
logger.error(
"ABORT!!! Out of all {} trainers, the trainer process with rank={} was aborted. Please check its log.".
format(nranks, error_rank))
terminate_local_procs(procs)
raise
return alive
python/paddle/fleet/runtime/runtime_base.py
浏览文件 @ 1731b32d
......@@ -19,11 +19,8 @@ class RuntimeBase(object):
def __init__(self):
pass
def _set_basic_info(self, loss, role_maker, optimizer, strategy):
self.loss = loss
self.role_maker = role_maker
self.optimizer = optimizer
self.strategy = strategy
def _set_basic_info(self, context):
self.context = context
def _run_worker(self):
pass
......
python/paddle/fluid/dygraph/layers.py
浏览文件 @ 1731b32d
......@@ -129,6 +129,45 @@ class Layer(core.Layer):
for layer in self.sublayers():
layer.eval()
def apply(self, fn):
"""
Applies ``fn`` recursively to every sublayer (as returned by ``.sublayers()``)
as well as self. Typical use includes initializing the parameters of a model.
Parameters:
fn (function): a function to be applied to each sublayer
Returns:
Layer: self
Example::
.. code-block:: python
import paddle
import paddle.nn as nn
paddle.enable_imperative()
net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
def init_weights(layer):
if type(layer) == nn.Linear:
print('before init weight:', layer.weight.numpy())
new_weight = paddle.fill_constant(layer.weight.shape, layer.weight.dtype, value=0.9)
layer.weight.set_value(new_weight)
print('after init weight:', layer.weight.numpy())
net.apply(init_weights)
print(net.state_dict())
"""
for layer in self.sublayers():
layer.apply(fn)
fn(self)
return self
def full_name(self):
"""Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 1731b32d
......@@ -27,6 +27,7 @@ list(APPEND MIXED_DIST_TEST_OPS test_communicator_async)
list(APPEND MIXED_DIST_TEST_OPS test_communicator_geo)
list(APPEND MIXED_DIST_TEST_OPS test_communicator_half_async)
list(APPEND MIXED_DIST_TEST_OPS test_communicator_sync)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_launch)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_api_input)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_checkpoint)
list(APPEND MIXED_DIST_TEST_OPS test_collective_optimizer)
......@@ -399,6 +400,7 @@ if(WITH_DISTRIBUTE)
py_test_modules(test_fleet_checkpoint MODULES test_fleet_checkpoint)
endif()
bash_test_modules(test_launch_ps MODULES test_launch_ps.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_fleet_launch MODULES test_fleet_launch.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
set(dist_ut_port 20001)
foreach(TEST_OP ${DIST_TEST_OPS})
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_bert.py
浏览文件 @ 1731b32d
......@@ -18,6 +18,7 @@ import unittest
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.dygraph.dygraph_to_static import ProgramTranslator
from paddle.fluid.dygraph.io import VARIABLE_FILENAME
from bert_dygraph_model import PretrainModelLayer
from bert_utils import get_bert_config, get_feed_data_reader
......@@ -28,9 +29,11 @@ place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() else fluid.CPUPlace(
SEED = 2020
STEP_NUM = 10
PRINT_STEP = 2
MODEL_SAVE_PATH = "./bert.inference.model"
DY_STATE_DICT_SAVE_PATH = "./bert.dygraph"
def train(bert_config, data_reader):
def train(bert_config, data_reader, to_static):
with fluid.dygraph.guard(place):
fluid.default_main_program().random_seed = SEED
fluid.default_startup_program().random_seed = SEED
......@@ -79,18 +82,74 @@ def train(bert_config, data_reader):
step_idx += 1
if step_idx == STEP_NUM:
if to_static:
fluid.dygraph.jit.save(bert, MODEL_SAVE_PATH)
else:
fluid.dygraph.save_dygraph(bert.state_dict(),
DY_STATE_DICT_SAVE_PATH)
break
return loss, ppl
def train_dygraph(bert_config, data_reader):
program_translator.enable(False)
return train(bert_config, data_reader)
return train(bert_config, data_reader, False)
def train_static(bert_config, data_reader):
program_translator.enable(True)
return train(bert_config, data_reader)
return train(bert_config, data_reader, True)
def predict_static(data):
exe = fluid.Executor(place)
# load inference model
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(
MODEL_SAVE_PATH, executor=exe, params_filename=VARIABLE_FILENAME)
pred_res = exe.run(inference_program,
feed=dict(zip(feed_target_names, data)),
fetch_list=fetch_targets)
return pred_res
def predict_dygraph(bert_config, data):
program_translator.enable(False)
with fluid.dygraph.guard(place):
bert = PretrainModelLayer(
config=bert_config, weight_sharing=False, use_fp16=False)
model_dict, _ = fluid.dygraph.load_dygraph(DY_STATE_DICT_SAVE_PATH)
bert.set_dict(model_dict)
bert.eval()
input_vars = [fluid.dygraph.to_variable(x) for x in data]
src_ids, pos_ids, sent_ids, input_mask, mask_label, mask_pos, labels = input_vars
pred_res = bert(
src_ids=src_ids,
position_ids=pos_ids,
sentence_ids=sent_ids,
input_mask=input_mask,
mask_label=mask_label,
mask_pos=mask_pos,
labels=labels)
pred_res = [var.numpy() for var in pred_res]
return pred_res
def predict_dygraph_jit(data):
with fluid.dygraph.guard(place):
bert = fluid.dygraph.jit.load(MODEL_SAVE_PATH)
bert.eval()
src_ids, pos_ids, sent_ids, input_mask, mask_label, mask_pos, labels = data
pred_res = bert(src_ids, pos_ids, sent_ids, input_mask, mask_label,
mask_pos, labels)
pred_res = [var.numpy() for var in pred_res]
return pred_res
class TestBert(unittest.TestCase):
......@@ -104,14 +163,36 @@ class TestBert(unittest.TestCase):
dygraph_loss, dygraph_ppl = train_dygraph(self.bert_config,
self.data_reader)
self.assertTrue(
np.allclose(static_loss, static_loss),
msg="static_loss: {} \n static_loss: {}".format(static_loss,
dygraph_loss))
np.allclose(static_loss, dygraph_loss),
msg="static_loss: {} \n dygraph_loss: {}".format(static_loss,
dygraph_loss))
self.assertTrue(
np.allclose(static_ppl, dygraph_ppl),
msg="static_ppl: {} \n dygraph_ppl: {}".format(static_ppl,
dygraph_ppl))
self.verify_predict()
def verify_predict(self):
for data in self.data_reader.data_generator()():
dygraph_pred_res = predict_dygraph(self.bert_config, data)
static_pred_res = predict_static(data)
dygraph_jit_pred_res = predict_dygraph_jit(data)
for dy_res, st_res, dy_jit_res in zip(
dygraph_pred_res, static_pred_res, dygraph_jit_pred_res):
self.assertTrue(
np.allclose(st_res, dy_res),
"dygraph_res: {},\n static_res: {}".format(
dy_res[~np.isclose(st_res, dy_res)],
st_res[~np.isclose(st_res, dy_res)]))
self.assertTrue(
np.allclose(st_res, dy_jit_res),
"dygraph_jit_res: {},\n static_res: {}".format(
dy_jit_res[~np.isclose(st_res, dy_jit_res)],
st_res[~np.isclose(st_res, dy_jit_res)]))
break
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/dygraph_to_static/test_bmn.py
浏览文件 @ 1731b32d
......@@ -692,13 +692,20 @@ class TestTrain(unittest.TestCase):
video_data = np.array([item[0] for item in data]).astype(DATATYPE)
static_pred_res = self.predict_static(video_data)
dygraph_pred_res = self.predict_dygraph(video_data)
dygraph_jit_pred_res = self.predict_dygraph_jit(video_data)
for dy_res, st_res in zip(dygraph_pred_res, static_pred_res):
for dy_res, st_res, dy_jit_res in zip(
dygraph_pred_res, static_pred_res, dygraph_jit_pred_res):
self.assertTrue(
np.allclose(st_res, dy_res),
"dygraph_res: {},\n static_res: {}".format(
dy_res[~np.isclose(st_res, dy_res)],
st_res[~np.isclose(st_res, dy_res)]))
self.assertTrue(
np.allclose(st_res, dy_jit_res),
"dygraph_jit_res: {},\n static_res: {}".format(
dy_jit_res[~np.isclose(st_res, dy_jit_res)],
st_res[~np.isclose(st_res, dy_jit_res)]))
break
def predict_dygraph(self, data):
......@@ -731,6 +738,17 @@ class TestTrain(unittest.TestCase):
return pred_res
def predict_dygraph_jit(self, data):
with fluid.dygraph.guard(self.place):
bmn = fluid.dygraph.jit.load(self.args.infer_dir)
bmn.eval()
x = to_variable(data)
pred_res = bmn(x)
pred_res = [var.numpy() for var in pred_res]
return pred_res
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/dygraph_to_static/test_lac.py
浏览文件 @ 1731b32d
......@@ -535,9 +535,14 @@ class TestLACModel(unittest.TestCase):
batch = [np.vstack(var) for var in zip(*batch)]
dy_pre = self.predict_dygraph(batch)
st_pre = self.predict_static(batch)
dy_jit_pre = self.predict_dygraph_jit(batch)
self.assertTrue(
np.allclose(dy_pre, st_pre),
msg="dy_pre:\n {}\n, st_pre: \n{}.".format(dy_pre, st_pre))
self.assertTrue(
np.allclose(dy_jit_pre, st_pre),
msg="dy_jit_pre:\n {}\n, st_pre: \n{}.".format(dy_jit_pre,
st_pre))
def predict_dygraph(self, batch):
words, targets, length = batch
......@@ -576,6 +581,16 @@ class TestLACModel(unittest.TestCase):
fetch_list=fetch_targets)
return pred_res[0]
def predict_dygraph_jit(self, batch):
words, targets, length = batch
with fluid.dygraph.guard(self.place):
model = fluid.dygraph.jit.load(self.args.model_save_dir)
model.eval()
pred_res = model(to_variable(words), to_variable(length))
return pred_res.numpy()
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py
浏览文件 @ 1731b32d
......@@ -19,6 +19,7 @@ from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle.fluid.dygraph import declarative, ProgramTranslator
from paddle.fluid.dygraph.io import VARIABLE_FILENAME
import unittest
......@@ -433,14 +434,15 @@ class Args(object):
class_dim = 50
print_step = 1
train_step = 10
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
) else fluid.CPUPlace()
model_save_path = model + ".inference.model"
dy_state_dict_save_path = model + ".dygraph"
def train_mobilenet(args, to_static):
program_translator.enable(to_static)
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
) else fluid.CPUPlace()
with fluid.dygraph.guard(place):
with fluid.dygraph.guard(args.place):
np.random.seed(SEED)
fluid.default_startup_program().random_seed = SEED
......@@ -461,7 +463,7 @@ def train_mobilenet(args, to_static):
# 3. reader
train_reader = fake_data_reader(args.batch_size, args.class_dim)
train_data_loader = fluid.io.DataLoader.from_generator(capacity=16)
train_data_loader.set_sample_list_generator(train_reader, place)
train_data_loader.set_sample_list_generator(train_reader)
# 4. train loop
loss_data = []
......@@ -498,17 +500,64 @@ def train_mobilenet(args, to_static):
batch_id += 1
t_last = time.time()
if batch_id > args.train_step:
if to_static:
fluid.dygraph.jit.save(net, args.model_save_path)
else:
fluid.dygraph.save_dygraph(net.state_dict(),
args.dy_state_dict_save_path)
break
return np.array(loss_data)
def predict_static(args, data):
exe = fluid.Executor(args.place)
# load inference model
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(
args.model_save_path, executor=exe, params_filename=VARIABLE_FILENAME)
pred_res = exe.run(inference_program,
feed={feed_target_names[0]: data},
fetch_list=fetch_targets)
return pred_res[0]
def predict_dygraph(args, data):
program_translator.enable(False)
with fluid.dygraph.guard(args.place):
if args.model == "MobileNetV1":
model = MobileNetV1(class_dim=args.class_dim, scale=1.0)
elif args.model == "MobileNetV2":
model = MobileNetV2(class_dim=args.class_dim, scale=1.0)
# load dygraph trained parameters
model_dict, _ = fluid.load_dygraph(args.dy_state_dict_save_path)
model.set_dict(model_dict)
model.eval()
pred_res = model(fluid.dygraph.to_variable(data))
return pred_res.numpy()
def predict_dygraph_jit(args, data):
with fluid.dygraph.guard(args.place):
model = fluid.dygraph.jit.load(args.model_save_path)
model.eval()
pred_res = model(data)
return pred_res.numpy()
class TestMobileNet(unittest.TestCase):
def setUp(self):
self.args = Args()
def train(self, model_name, to_static):
self.args.model = model_name
self.args.model_save_path = model_name + ".inference.model"
self.args.dy_state_dict_save_path = model_name + ".dygraph"
out = train_mobilenet(self.args, to_static)
return out
......@@ -519,12 +568,36 @@ class TestMobileNet(unittest.TestCase):
np.allclose(dy_out, st_out),
msg="dy_out: {}, st_out: {}".format(dy_out, st_out))
def test_mobileNet(self):
def assert_same_predict(self, model_name):
self.args.model = model_name
self.args.model_save_path = model_name + ".inference.model"
self.args.dy_state_dict_save_path = model_name + ".dygraph"
local_random = np.random.RandomState(SEED)
image = local_random.random_sample([1, 3, 224, 224]).astype('float32')
dy_pre = predict_dygraph(self.args, image)
st_pre = predict_static(self.args, image)
dy_jit_pre = predict_dygraph_jit(self.args, image)
self.assertTrue(
np.allclose(dy_pre, st_pre),
msg="dy_pre:\n {}\n, st_pre: \n{}.".format(dy_pre, st_pre))
self.assertTrue(
np.allclose(dy_jit_pre, st_pre),
msg="dy_jit_pre:\n {}\n, st_pre: \n{}.".format(dy_jit_pre, st_pre))
def test_mobile_net(self):
# MobileNet-V1
self.assert_same_loss("MobileNetV1")
# MobileNet-V2
self.assert_same_loss("MobileNetV2")
self.verify_predict()
def verify_predict(self):
# MobileNet-V1
self.assert_same_predict("MobileNetV1")
# MobileNet-V2
self.assert_same_predict("MobileNetV2")
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py
浏览文件 @ 1731b32d
......@@ -22,39 +22,33 @@ import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.jit import dygraph_to_static_func
from paddle.fluid.dygraph import declarative, ProgramTranslator
from paddle.fluid.dygraph.nn import BatchNorm, Conv2D, Linear, Pool2D
from paddle.fluid.dygraph.io import VARIABLE_FILENAME
SEED = 2020
IMAGENET1000 = 1281167
base_lr = 0.1
base_lr = 0.001
momentum_rate = 0.9
l2_decay = 1e-4
batch_size = 8
epoch_num = 1
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() \
else fluid.CPUPlace()
MODEL_SAVE_PATH = "./resnet.inference.model"
DY_STATE_DICT_SAVE_PATH = "./resnet.dygraph"
program_translator = ProgramTranslator()
if fluid.is_compiled_with_cuda():
fluid.set_flags({'FLAGS_cudnn_deterministic': True})
def optimizer_setting(parameter_list=None):
total_images = IMAGENET1000
step = int(math.ceil(float(total_images) / batch_size))
epochs = [30, 60, 90]
bd = [step * e for e in epochs]
lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
if fluid.in_dygraph_mode():
optimizer = fluid.optimizer.Momentum(
learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr),
momentum=momentum_rate,
regularization=fluid.regularizer.L2Decay(l2_decay),
parameter_list=parameter_list)
else:
optimizer = fluid.optimizer.Momentum(
learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr),
momentum=momentum_rate,
regularization=fluid.regularizer.L2Decay(l2_decay))
optimizer = fluid.optimizer.Momentum(
learning_rate=base_lr,
momentum=momentum_rate,
regularization=fluid.regularizer.L2Decay(l2_decay),
parameter_list=parameter_list)
return optimizer
......@@ -189,8 +183,8 @@ class ResNet(fluid.dygraph.Layer):
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv)))
@dygraph_to_static_func
def forward(self, inputs, label):
@declarative
def forward(self, inputs):
y = self.conv(inputs)
y = self.pool2d_max(y)
for bottleneck_block in self.bottleneck_block_list:
......@@ -199,77 +193,144 @@ class ResNet(fluid.dygraph.Layer):
y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
pred = self.out(y)
loss = fluid.layers.cross_entropy(input=pred, label=label)
avg_loss_ = fluid.layers.mean(x=loss)
acc_top1_ = fluid.layers.accuracy(input=pred, label=label, k=1)
acc_top5_ = fluid.layers.accuracy(input=pred, label=label, k=5)
return pred
return pred, avg_loss_, acc_top1_, acc_top5_
def reader_decorator(reader):
def __reader__():
for item in reader():
img = np.array(item[0]).astype('float32').reshape(3, 224, 224)
label = np.array(item[1]).astype('int64').reshape(1)
yield img, label
return __reader__
def train_resnet_in_static_mode():
def train(to_static):
"""
Tests model decorated by `dygraph_to_static_output` in static mode. For users, the model is defined in dygraph mode and trained in static mode.
"""
with fluid.dygraph.guard(place):
np.random.seed(SEED)
fluid.default_startup_program().random_seed = SEED
fluid.default_main_program().random_seed = SEED
train_reader = paddle.batch(
reader_decorator(paddle.dataset.flowers.train(use_xmap=False)),
batch_size=batch_size,
drop_last=True)
data_loader = fluid.io.DataLoader.from_generator(
capacity=5, iterable=True)
data_loader.set_sample_list_generator(train_reader)
resnet = ResNet()
optimizer = optimizer_setting(parameter_list=resnet.parameters())
for epoch in range(epoch_num):
total_loss = 0.0
total_acc1 = 0.0
total_acc5 = 0.0
total_sample = 0
for batch_id, data in enumerate(data_loader()):
start_time = time.time()
img, label = data
pred = resnet(img)
loss = fluid.layers.cross_entropy(input=pred, label=label)
avg_loss = fluid.layers.mean(x=loss)
acc_top1 = fluid.layers.accuracy(input=pred, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=pred, label=label, k=5)
avg_loss.backward()
optimizer.minimize(avg_loss)
resnet.clear_gradients()
total_loss += avg_loss
total_acc1 += acc_top1
total_acc5 += acc_top5
total_sample += 1
end_time = time.time()
if batch_id % 2 == 0:
print( "epoch %d | batch step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, time %f" % \
( epoch, batch_id, total_loss.numpy() / total_sample, \
total_acc1.numpy() / total_sample, total_acc5.numpy() / total_sample, end_time-start_time))
if batch_id == 10:
if to_static:
fluid.dygraph.jit.save(resnet, MODEL_SAVE_PATH)
else:
fluid.dygraph.save_dygraph(resnet.state_dict(),
DY_STATE_DICT_SAVE_PATH)
# avoid dataloader throw abort signaal
data_loader._reset()
break
return total_loss.numpy()
def predict_dygraph(data):
program_translator.enable(False)
with fluid.dygraph.guard(place):
resnet = ResNet()
model_dict, _ = fluid.dygraph.load_dygraph(DY_STATE_DICT_SAVE_PATH)
resnet.set_dict(model_dict)
resnet.eval()
pred_res = resnet(fluid.dygraph.to_variable(data))
return pred_res.numpy()
def predict_static(data):
exe = fluid.Executor(place)
startup_prog = fluid.Program()
main_prog = fluid.Program()
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(
MODEL_SAVE_PATH, executor=exe, params_filename=VARIABLE_FILENAME)
with fluid.program_guard(main_prog, startup_prog):
pred_res = exe.run(inference_program,
feed={feed_target_names[0]: data},
fetch_list=fetch_targets)
img = fluid.data(name="img", shape=[None, 3, 224, 224], dtype="float32")
label = fluid.data(name="label", shape=[None, 1], dtype="int64")
label.stop_gradient = True
resnet = ResNet()
pred, avg_loss_, acc_top1_, acc_top5_ = resnet(img, label)
optimizer = optimizer_setting(parameter_list=resnet.parameters())
optimizer.minimize(avg_loss_)
exe.run(startup_prog)
train_reader = paddle.batch(
paddle.dataset.flowers.train(use_xmap=False), batch_size=batch_size)
for epoch in range(epoch_num):
total_loss = 0.0
total_acc1 = 0.0
total_acc5 = 0.0
total_sample = 0
for batch_id, data in enumerate(train_reader()):
start_time = time.time()
dy_x_data = np.array(
[x[0].reshape(3, 224, 224) for x in data]).astype('float32')
if len(np.array([x[1]
for x in data]).astype('int64')) != batch_size:
continue
y_data = np.array([x[1] for x in data]).astype('int64').reshape(-1,
1)
avg_loss, acc_top1, acc_top5 = exe.run(
main_prog,
feed={"img": dy_x_data,
"label": y_data},
fetch_list=[avg_loss_, acc_top1_, acc_top5_])
total_loss += avg_loss
total_acc1 += acc_top1
total_acc5 += acc_top5
total_sample += 1
end_time = time.time()
if batch_id % 2 == 0:
print( "epoch %d | batch step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, time %f" % \
( epoch, batch_id, total_loss / total_sample, \
total_acc1 / total_sample, total_acc5 / total_sample, end_time-start_time))
if batch_id == 10:
break
return pred_res[0]
def predict_dygraph_jit(data):
with fluid.dygraph.guard(place):
resnet = fluid.dygraph.jit.load(MODEL_SAVE_PATH)
resnet.eval()
pred_res = resnet(data)
return pred_res.numpy()
class TestResnet(unittest.TestCase):
def test_in_static_mode(self):
train_resnet_in_static_mode()
def train(self, to_static):
program_translator.enable(to_static)
return train(to_static)
def verify_predict(self):
image = np.random.random([1, 3, 224, 224]).astype('float32')
dy_pre = predict_dygraph(image)
st_pre = predict_static(image)
dy_jit_pre = predict_dygraph_jit(image)
self.assertTrue(
np.allclose(dy_pre, st_pre),
msg="dy_pre:\n {}\n, st_pre: \n{}.".format(dy_pre, st_pre))
self.assertTrue(
np.allclose(dy_jit_pre, st_pre),
msg="dy_jit_pre:\n {}\n, st_pre: \n{}.".format(dy_jit_pre, st_pre))
def test_resnet(self):
static_loss = self.train(to_static=True)
dygraph_loss = self.train(to_static=False)
self.assertTrue(
np.allclose(static_loss, dygraph_loss),
msg="static_loss: {} \n dygraph_loss: {}".format(static_loss,
dygraph_loss))
self.verify_predict()
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py
浏览文件 @ 1731b32d
......@@ -24,6 +24,7 @@ from paddle.fluid.dygraph.base import to_variable
from paddle.fluid.dygraph.nn import BatchNorm, Conv2D, Linear, Pool2D
from paddle.fluid.dygraph import declarative
from paddle.fluid.dygraph import ProgramTranslator
from paddle.fluid.dygraph.io import VARIABLE_FILENAME
SEED = 2020
np.random.seed(SEED)
......@@ -32,6 +33,8 @@ BATCH_SIZE = 8
EPOCH_NUM = 1
PRINT_STEP = 2
STEP_NUM = 10
MODEL_SAVE_PATH = "./se_resnet.inference.model"
DY_STATE_DICT_SAVE_PATH = "./se_resnet.dygraph"
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() \
else fluid.CPUPlace()
......@@ -377,11 +380,60 @@ def train(train_reader, to_static):
step_idx += 1
if step_idx == STEP_NUM:
if to_static:
configs = fluid.dygraph.jit.SaveLoadConfig()
configs.output_spec = [pred]
fluid.dygraph.jit.save(se_resnext, MODEL_SAVE_PATH,
[img], configs)
else:
fluid.dygraph.save_dygraph(se_resnext.state_dict(),
DY_STATE_DICT_SAVE_PATH)
break
return pred.numpy(), avg_loss.numpy(), acc_top1.numpy(), acc_top5.numpy(
)
def predict_dygraph(data):
program_translator = ProgramTranslator()
program_translator.enable(False)
with fluid.dygraph.guard(place):
se_resnext = SeResNeXt()
model_dict, _ = fluid.dygraph.load_dygraph(DY_STATE_DICT_SAVE_PATH)
se_resnext.set_dict(model_dict)
se_resnext.eval()
label = np.random.random([1, 1]).astype("int64")
img = fluid.dygraph.to_variable(data)
label = fluid.dygraph.to_variable(label)
pred_res, _, _, _ = se_resnext(img, label)
return pred_res.numpy()
def predict_static(data):
exe = fluid.Executor(place)
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(
MODEL_SAVE_PATH, executor=exe, params_filename=VARIABLE_FILENAME)
pred_res = exe.run(inference_program,
feed={feed_target_names[0]: data},
fetch_list=fetch_targets)
return pred_res[0]
def predict_dygraph_jit(data):
with fluid.dygraph.guard(place):
se_resnext = fluid.dygraph.jit.load(MODEL_SAVE_PATH)
se_resnext.eval()
pred_res = se_resnext(data)
return pred_res.numpy()
class TestSeResnet(unittest.TestCase):
def setUp(self):
self.train_reader = paddle.batch(
......@@ -390,6 +442,18 @@ class TestSeResnet(unittest.TestCase):
batch_size=BATCH_SIZE,
drop_last=True)
def verify_predict(self):
image = np.random.random([1, 3, 224, 224]).astype('float32')
dy_pre = predict_dygraph(image)
st_pre = predict_static(image)
dy_jit_pre = predict_dygraph_jit(image)
self.assertTrue(
np.allclose(dy_pre, st_pre),
msg="dy_pre:\n {}\n, st_pre: \n{}.".format(dy_pre, st_pre))
self.assertTrue(
np.allclose(dy_jit_pre, st_pre),
msg="dy_jit_pre:\n {}\n, st_pre: \n{}.".format(dy_jit_pre, st_pre))
def test_check_result(self):
pred_1, loss_1, acc1_1, acc5_1 = train(
self.train_reader, to_static=False)
......@@ -409,6 +473,8 @@ class TestSeResnet(unittest.TestCase):
np.allclose(acc5_1, acc5_2),
msg="static acc5: {} \ndygraph acc5: {}".format(acc5_1, acc5_2))
self.verify_predict()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_compare_op.py
浏览文件 @ 1731b32d
......@@ -72,25 +72,40 @@ def create_paddle_case(op_type, callback):
class PaddleCls(unittest.TestCase):
def setUp(self):
self.op_type = op_type
self.input_x = np.array([1, 2, 3, 4])
self.input_y = np.array([1, 3, 2, 4])
self.input_x = np.array([1, 2, 3, 4]).astype(np.int64)
self.input_y = np.array([1, 3, 2, 4]).astype(np.int64)
self.real_result = callback(self.input_x, self.input_y)
self.place = fluid.CPUPlace()
if core.is_compiled_with_cuda():
self.place = paddle.CUDAPlace(0)
def test_api(self):
with program_guard(Program(), Program()):
x = fluid.layers.data(name='x', shape=[4], dtype='int64')
y = fluid.layers.data(name='y', shape=[4], dtype='int64')
x = fluid.data(name='x', shape=[4], dtype='int64')
y = fluid.data(name='y', shape=[4], dtype='int64')
op = eval("paddle.%s" % (self.op_type))
out = op(x, y)
place = fluid.CPUPlace()
if core.is_compiled_with_cuda():
place = paddle.CUDAPlace(0)
exe = fluid.Executor(place)
exe = fluid.Executor(self.place)
res, = exe.run(feed={"x": self.input_x,
"y": self.input_y},
fetch_list=[out])
self.assertEqual((res == self.real_result).all(), True)
def test_broadcast_api_1(self):
with program_guard(Program(), Program()):
x = paddle.nn.data(name='x', shape=[1, 2, 1, 3], dtype='int32')
y = paddle.nn.data(name='y', shape=[1, 2, 3], dtype='int32')
op = eval("paddle.%s" % (self.op_type))
out = op(x, y)
exe = paddle.Executor(self.place)
input_x = np.arange(1, 7).reshape((1, 2, 1, 3)).astype(np.int32)
input_y = np.arange(0, 6).reshape((1, 2, 3)).astype(np.int32)
real_result = callback(input_x, input_y)
res, = exe.run(feed={"x": input_x,
"y": input_y},
fetch_list=[out])
self.assertEqual((res == real_result).all(), True)
def test_attr_name(self):
with program_guard(Program(), Program()):
x = fluid.layers.data(name='x', shape=[4], dtype='int32')
......@@ -104,6 +119,7 @@ def create_paddle_case(op_type, callback):
globals()[cls_name] = PaddleCls
create_paddle_case('less_than', lambda _a, _b: _a < _b)
create_paddle_case('less_equal', lambda _a, _b: _a <= _b)
create_paddle_case('greater_than', lambda _a, _b: _a > _b)
create_paddle_case('greater_equal', lambda _a, _b: _a >= _b)
......
python/paddle/fluid/tests/unittests/test_fleet_launch.sh 0 → 100644
浏览文件 @ 1731b32d
#!/bin/bash
set -e
function test_launch_ps(){
fleetrun --server_num=2 --worker_num=2 fleet_ps_training.py 2> ut.elog
if grep -q "server are killed" ut.elog; then
echo "test pserver launch succeed"
else
echo "test pserver launch failed"
exit -1
fi
}
if [[ ${WITH_GPU} == "OFF" ]]; then
test_launch_ps
exit 0
fi
test_launch_ps
# use default values
fleetrun multi_process.py
# use paddlecloud
echo "begin test use paddlecloud"
cluster_node_ips="127.0.0.1,127.0.0.2"
export PADDLE_TRAINERS_NUM=2
export POD_IP=127.0.0.1
export PADDLE_TRAINERS=127.0.0.1,127.0.0.2
export PADDLE_TRAINER_ID=0
export PADDLE_PORT=35019
export TRAINER_PORTS_NUM=2
distributed_args="--ips=${cluster_node_ips} --gpus=0,1 --log_dir=testlog"
CUDA_VISIBLE_DEVICES=0,1 fleetrun ${distributed_args} multi_process.py
str1="selected_gpus:0 worker_endpoints:127.0.0.1:35019,127.0.0.1:35020,127.0.0.2:35019,127.0.0.2:35020 trainers_num:4 current_endpoint:127.0.0.1:35019 trainer_id:0"
str2="selected_gpus:1 worker_endpoints:127.0.0.1:35019,127.0.0.1:35020,127.0.0.2:35019,127.0.0.2:35020 trainers_num:4 current_endpoint:127.0.0.1:35020 trainer_id:1"
file_0="multi_process.check_0.log"
file_1="multi_process.check_1.log"
echo "paddlecloud params test"
if grep -q "$str1" "$file_0"; then
echo "find trainer 0"
else
echo "not find trainer 0"
exit -1
fi
if grep -q "$str2" "$file_1"; then
echo "find trainer 1"
else
echo "not find trainer 1"
exit -1
fi
# test async poll process
if [ -f $file_0 ]; then
rm $file_0
fi
if [ -f $file_1 ]; then
rm $file_1
fi
unset PADDLE_PORT
unset TRAINER_PORTS_NUM
echo ""
echo "paddle.distributed.launch async poll process test"
if ! CUDA_VISIBLE_DEVICES=0,1 fleetrun ${distributed_args} multi_process.py abort; then
echo "train abort as planned"
fi
abort_str1="abort>>> selected_gpus:0 worker_endpoints:127.0.0.1:6170,127.0.0.1:6171,127.0.0.2:6170,127.0.0.2:6171 trainers_num:4 current_endpoint:127.0.0.1:6170 trainer_id:0"
if grep -q "$abort_str1" "$file_0"; then
echo "trainer 0 abort as planned"
else
echo "trainer 0 not abort as planned"
exit -1
fi
if [ ! -f $file_1 ]; then
echo "trainer 1 terminate as planned"
else
echo "trainer 1 not terminate as planned"
exit -1
fi
#test for random ports
file_0_0="test_launch_filelock_0_0.log"
file_1_0="test_launch_filelock_1_0.log"
rm -rf $file_0_0 $file_0_1
distributed_args="--gpus=0,1 --log_dir=testlog"
export PADDLE_LAUNCH_LOG="test_launch_filelock_0"
CUDA_VISIBLE_DEVICES=0,1 fleetrun ${distributed_args} find_ports.py
str_0="worker_endpoints:127.0.0.1:6070,127.0.0.1:6071"
python/paddle/fluid/tests/unittests/test_fleet_util.py
浏览文件 @ 1731b32d
......@@ -33,8 +33,10 @@ class TestFleetUtil(unittest.TestCase):
role_maker = None # should be fleet.PaddleCloudRoleMaker()
optimize_ops = []
params_grads = []
util = factory._create_util(strategy, role_maker, optimize_ops,
params_grads)
context = {}
context["role_maker"] = role_maker
context["valid_strategy"] = strategy
util = factory._create_util(context)
self.assertEqual(util.role_maker, None)
def test_get_util(self):
......
python/paddle/fluid/tests/unittests/test_imperative_double_grad.py
浏览文件 @ 1731b32d
......@@ -298,16 +298,15 @@ class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):
class TestDygraphDoubleGradVisitedUniq(TestCase):
def test_compare(self):
value = np.random.uniform(-0.5, 0.5, 100).reshape(10, 2,
5).astype("float32")
value = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
def model_f(input):
linear = fluid.dygraph.Linear(5, 3, bias_attr=False)
conv2d = fluid.dygraph.Conv2D(3, 2, 3)
for i in range(10):
if i == 0:
out = linear(input)
out = conv2d(input)
else:
out = out + linear(input)
out = out + conv2d(input)
return out
backward_strategy = fluid.dygraph.BackwardStrategy()
......@@ -319,8 +318,14 @@ class TestDygraphDoubleGradVisitedUniq(TestCase):
out = model_f(a)
dx=fluid.dygraph.grad(outputs=[out],inputs=[a],create_graph=True,retain_graph=True, \
only_inputs=True,allow_unused=False, backward_strategy=backward_strategy)
dx = fluid.dygraph.grad(
outputs=[out],
inputs=[a],
create_graph=True,
retain_graph=True,
only_inputs=True,
allow_unused=False,
backward_strategy=backward_strategy)
grad_1 = dx[0].numpy()
......@@ -334,7 +339,9 @@ class TestDygraphDoubleGradVisitedUniq(TestCase):
grad_2 = a.gradient()
self.assertTrue(np.array_equal(grad_1, grad_2))
self.assertTrue(
np.allclose(
grad_1, grad_2, rtol=1.e-5, atol=1.e-8, equal_nan=True))
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_imperative_layer_apply.py 0 → 100644
浏览文件 @ 1731b32d
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import paddle
import paddle.nn as nn
import paddle.fluid as fluid
import numpy as np
class LeNetDygraph(fluid.dygraph.Layer):
def __init__(self, num_classes=10, classifier_activation='softmax'):
super(LeNetDygraph, self).__init__()
self.num_classes = num_classes
self.features = nn.Sequential(
nn.Conv2D(
1, 6, 3, stride=1, padding=1),
nn.ReLU(),
nn.Pool2D(2, 'max', 2),
nn.Conv2D(
6, 16, 5, stride=1, padding=0),
nn.ReLU(),
nn.Pool2D(2, 'max', 2))
if num_classes > 0:
self.fc = nn.Sequential(
nn.Linear(400, 120),
nn.Linear(120, 84),
nn.Linear(
84, 10, act=classifier_activation))
def forward(self, inputs):
x = self.features(inputs)
if self.num_classes > 0:
x = fluid.layers.flatten(x, 1)
x = self.fc(x)
return x
def init_weights(layer):
if type(layer) == nn.Linear:
new_weight = paddle.fill_constant(
layer.weight.shape, layer.weight.dtype, value=0.9)
layer.weight.set_value(new_weight)
new_bias = paddle.fill_constant(
layer.bias.shape, layer.bias.dtype, value=-0.1)
layer.bias.set_value(new_bias)
elif type(layer) == nn.Conv2D:
new_weight = paddle.fill_constant(
layer.weight.shape, layer.weight.dtype, value=0.7)
layer.weight.set_value(new_weight)
new_bias = paddle.fill_constant(
layer.bias.shape, layer.bias.dtype, value=-0.2)
layer.bias.set_value(new_bias)
class TestLayerApply(unittest.TestCase):
def test_apply_init_weight(self):
with fluid.dygraph.guard():
net = LeNetDygraph()
net.apply(init_weights)
for layer in net.sublayers():
if type(layer) == nn.Linear:
np.testing.assert_allclose(layer.weight.numpy(), 0.9)
np.testing.assert_allclose(layer.bias.numpy(), -0.1)
elif type(layer) == nn.Conv2D:
np.testing.assert_allclose(layer.weight.numpy(), 0.7)
np.testing.assert_allclose(layer.bias.numpy(), -0.2)
if __name__ == '__main__':
unittest.main()
python/setup.py.in
浏览文件 @ 1731b32d
......@@ -475,6 +475,11 @@ with redirect_stdout():
cmdclass={
'install_headers': InstallHeaders,
'install': InstallCommand,
},
entry_points={
'console_scripts': [
'fleetrun = paddle.fleet.launch:launch'
]
}
)
......
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